text:
Simulation of a new hybrid particle swarm optimization algorithm
Mathew Mithra Noel; Thomas C. Jannett
In this paper a new hybrid particle swarm optimization (PSO) algorithm is introduced which makes use of gradient information to achieve faster convergence without getting trapped in local minima. Simulation results comparing the standard PSO algorithm to the new hybrid PSO algorithm are presented. The De Jong test suite of optimization problems is used to test the performance of all
Optimality Zone Algorithms for Hybrid Systems: Efficient Algorithms for Optimal Location and Control Computation
Peter E. Caines; M. Shahid Shaikh
\\u000a A general Hybrid Minimum Principle (HMP) for hybrid optimal control problems (HOCPs) is presented in [1, 2, 3, 4] and in [4,\\u000a 5], a class of efficient, provably convergent Hybrid Minimum Principle (HMP) algorithms were obtained based upon the HMP.\\u000a The notion of optimality zones (OZs) ([3, 4]) provides a theoretical framework for the computation of optimal location (i.e.\\u000a discrete
Improved hybrid optimization algorithm for 3D protein structure prediction.
Zhou, Changjun; Hou, Caixia; Wei, Xiaopeng; Zhang, Qiang
A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins. PMID:25069136
Pseudo-Tree Based Hybrid Algorithm for Distributed Constraint Optimization
Yeoh, William
Pseudo-Tree Based Hybrid Algorithm for Distributed Constraint Optimization Tenda Okimoto , Makoto-agent cooperation. Considering pseudo-tree based search algo- rithms is important in DCOPs, since their memory, how to speed up pseudo-tree based search al- gorithms is one of the major issues in DCOPs
A hybrid artificial bee colony algorithm for numerical function optimization
NASA Astrophysics Data System (ADS)
Alqattan, Zakaria N.; Abdullah, Rosni
Artificial Bee Colony (ABC) algorithm is one of the swarm intelligence algorithms; it has been introduced by Karaboga in 2005. It is a meta-heuristic optimization search algorithm inspired from the intelligent foraging behavior of the honey bees in nature. Its unique search process made it as one of the most competitive algorithm with some other search algorithms in the area of optimization, such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO). However, the ABC performance of the local search process and the bee movement or the solution improvement equation still has some weaknesses. The ABC is good in avoiding trapping at the local optimum but it spends its time searching around unpromising random selected solutions. Inspired by the PSO, we propose a Hybrid Particle-movement ABC algorithm called HPABC, which adapts the particle movement process to improve the exploration of the original ABC algorithm. Numerical benchmark functions were used in order to experimentally test the HPABC algorithm. The results illustrate that the HPABC algorithm can outperform the ABC algorithm in most of the experiments (75% better in accuracy and over 3 times faster).
Operational Optimal Ship Routing Using a Hybrid Parallel Genetic Algorithm
O. T. Kosmas; D. S. Vlachos
Optimization of ship routing depends on several parameters, like ship and cargo characteristics, environmental factors, topography, international navigation rules, crew comfort etc. The complex nature of the problem leads to oversimplifications in analytical techniques, while stochastic methods like simulated annealing can be both time consuming and sensitive to local minima. In this work, a hybrid parallel genetic algorithm - estimation of distribution algorithm is developed in the island model, to operationally calculate the optimal ship routing. The technique, which is applicable not only to clusters but to grids as well, is very fast and has been applied to very difficult environments, like the Greek seas with thousands of islands and extreme micro-climate conditions.
A Hybrid Swarm Algorithm for optimizing glaucoma diagnosis.
Raja, Chandrasekaran; Gangatharan, Narayanan
Glaucoma is among the most common causes of permanent blindness in human. Because the initial symptoms are not evident, mass screening would assist early diagnosis in the vast population. Such mass screening requires an automated diagnosis technique. Our proposed automation consists of pre-processing, optimal wavelet transformation, feature extraction, and classification modules. The hyper analytic wavelet transformation (HWT) based statistical features are extracted from fundus images. Because HWT preserves phase information, it is appropriate for feature extraction. The features are then classified by a Support Vector Machine (SVM) with a radial basis function (RBF) kernel. The filter coefficients of the wavelet transformation process and the SVM-RB width parameter are simultaneously tailored to best-fit the diagnosis by the hybrid Particle Swarm algorithm. To overcome premature convergence, a Group Search Optimizer (GSO) random searching (ranging) and area scanning behavior (around the optima) are embedded within the Particle Swarm Optimization (PSO) framework. We also embed a novel potential-area scanning as a preventive mechanism against premature convergence, rather than diagnosis and cure. This embedding does not compromise the generality and utility of PSO. In two 10-fold cross-validated test runs, the diagnostic accuracy of the proposed hybrid PSO exceeded that of conventional PSO. Furthermore, the hybrid PSO maintained the ability to explore even at later iterations, ensuring maturity in fitness. PMID:26093787
Optimizing remediation of an unconfined aquifer using a hybrid algorithm.
Hsiao, Chin-Tsai; Chang, Liang-Cheng
We present a novel hybrid algorithm, integrating a genetic algorithm (GA) and constrained differential dynamic programming (CDDP), to achieve remediation planning for an unconfined aquifer. The objective function includes both fixed and dynamic operation costs. GA determines the primary structure of the proposed algorithm, and a chromosome therein implemented by a series of binary digits represents a potential network design. The time-varying optimal operation cost associated with the network design is computed by the CDDP, in which is embedded a numerical transport model. Several computational approaches, including a chromosome bookkeeping procedure, are implemented to alleviate computational loading. Additionally, case studies that involve fixed and time-varying operating costs for confined and unconfined aquifers, respectively, are discussed to elucidate the effectiveness of the proposed algorithm. Simulation results indicate that the fixed costs markedly affect the optimal design, including the number and locations of the wells. Furthermore, the solution obtained using the confined approximation for an unconfined aquifer may be infeasible, as determined by an unconfined simulation. PMID:16324011
The Application of Hybrid Genetic Particle Swarm Optimization Algorithm in the Distribution Network Reconfigurations Multi-Objective Optimization
Caiqing Zhang; Jingjing Zhang; Xihua Gu
According to the single performance of most distribution network reconfigurations (DNR), this paper presents the multi-objective distribution network optimization model with the optimal network loss, load balancing, and power supply voltage. Combined with the evolution idea of genetic algorithm (GA) and population intellectual technique of particle swarm optimization (PSO) algorithm, it applies hybrid genetic particle swarm optimization algorithm (HGPSOA) to
Central Force Optimization: Nelder-Mead Hybrid Algorithm for Rectangular Microstrip Antenna Design
K. R. Mahmoud
In this article, an efficient global hybrid optimization method is proposed combining central force optimization as a global optimizer and the Nelder-Mead algorithm as a local optimizer. After the final global iteration, a local optimization can be followed to further improve the solution obtained from central force optimization. The convergence capability of the hybrid central force optimization�Nelder-Mead approach is compared
A hybrid artificial bee colony optimization and quantum evolutionary algorithm for continuous optimization problems.
Duan, Hai-Bin; Xu, Chun-Fang; Xing, Zhi-Hui
In this paper, a novel hybrid Artificial Bee Colony (ABC) and Quantum Evolutionary Algorithm (QEA) is proposed for solving continuous optimization problems. ABC is adopted to increase the local search capacity as well as the randomness of the populations. In this way, the improved QEA can jump out of the premature convergence and find the optimal value. To show the performance of our proposed hybrid QEA with ABC, a number of experiments are carried out on a set of well-known Benchmark continuous optimization problems and the related results are compared with two other QEAs: the QEA with classical crossover operation, and the QEA with 2-crossover strategy. The experimental comparison results demonstrate that the proposed hybrid ABC and QEA approach is feasible and effective in solving complex continuous optimization problems. PMID:20180252
Hybrid particle swarm optimization algorithm for solving systems of nonlinear equations
Aijia Ouyang; Yongquan Zhou; Qifang Luo
A hybrid particle swarm optimization (HPSO) algorithm, which combines the advantages of Nelder-Mead simplex method (SM) and particle swarm optimization (PSO) algorithm, is put forward to solve systems of nonlinear equations, and it can be used to overcome the difficulty in selecting good initial guess for SM and inaccuracy of PSO due to being easily trapped into local optimal. The
Microcomputer implementation of optimal algorithms for closed-loop control of hybrid stepper motor drives
P. Crnosija; Branislav Kuzmanovic; S. Ajdukovic
This paper discusses optimal algorithms for closed-loop control of hybrid stepper motor drives and their microprocessor implementation. The torque characteristics and the optimal control angle of hybrid stepper motor drives with added series resistance and reluctant stepper motor drives have been described in detail in the literature. The specific contribution of the paper to this field of research consists of
Overall Mission Trajectory Optimization for Manned Lunar Landing Mission Using a Hybrid Algorithm
Wende Huang; Wei Wang; Xiaoning Xi
Aimed at trajectory optimization problem for manned lunar landing mission, an overall mission optimization model was established, through which translunar trajectory, lunar parking orbit and transearth trajectory were designed optimally based on patched-conic technique. A hybrid approach combined genetic algorithm (GA) and sequential quadratic programming (SQP) was proposed to solve the optimization problem, in which the GA was used to
Optimizing Hydropower Reservoir Operation Using Hybrid Genetic Algorithm and Chaos
Chun-Tian Cheng; Wen-Chuan Wang; Dong-Mei Xu; K. W. Chau
Genetic algorithms (GA) have been widely applied to solve water resources system optimization. With the increase of the complexity\\u000a and the larger problem scale of water resources system, GAs are most frequently faced with the problems of premature convergence,\\u000a slow iterations to reach the global optimal solution and getting stuck at a local optimum. A novel chaos genetic algorithm\\u000a (CGA)
Synthesis of fiber Bragg grating parameters using hybrid optimization algorithm
C. W. Teoh; Faidz A. Rahman
A simple, fast and accurate method is proposed to reconstruct the parameters of fiber Bragg grating (FBG) from its reflectivity. The proposed method is based on hybrid of tabu search (TS) and Nelder-Mead (NM) simplex algorithm. This method allows wide coverage of the parameters' solution space and minimizes the risk of trapping in local minima. Tabu search explores the solution
Rolling Bearing Fault Diagnosis Based on the Hybrid Algorithm of Particle Swarm Optimization with Neighborhood Operator
Jia-tang Cheng; Li Ai; Wei Xiong
In order to improve the accuracy of rolling bearing fault diagnosis, a hybrid algorithm of particle swarm optimization with neighborhood operator is applied. According to the fault feature vectors, PSO with neighborhood operator is applied to optimize the weight of BP neural network, then the fault diagnosis is accomplished via the optimized neural network. The simulation results show that this
Optimization of a total internal reflection lens by using a hybrid Taguchi-simulated annealing algorithm
NASA Astrophysics Data System (ADS)
Chao, Shih-Min; Whang, Allen Jong-Woei; Chou, Chun-Han; Su, Wei-Shao; Hsieh, Tsung-Heng
In this paper, we propose a new method for optimization of a total internal reflection (TIR) lens by using a hybrid Taguchi-simulated annealing algorithm. The conventional simulated annealing (SA) algorithm is a method for solving global optimization problems and has also been used in non-imaging systems in recent years. However, the success of SA depends heavily on the annealing schedule and initial parameter setting. In this study, we successfully incorporated the Taguchi method into the SA algorithm. The new hybrid Taguchi-simulated annealing algorithm provides more precise search results and has lower initial parameter dependence.
Hybrid differential evolution and Nelder-Mead algorithm with re-optimization
Zhenxiao Gao; Tianyuan Xiao; Wenhui Fan
Nonlinear optimization algorithms could be divided into local exploitation methods such as Nelder�Mead (NM) algorithm and\\u000a global exploration ones, such as differential evolution (DE). The former searches fast yet could be easily trapped by local\\u000a optimum, whereas the latter possesses better convergence quality. This paper proposes hybrid differential evolution and NM\\u000a algorithm with re-optimization, called as DE-NMR. At first a
Hybrid Particle Swarm Optimization: An Examination of the Influence of Iterative Improvement Algorithms on Performance
Jens Gimmler; Thomas St�tzle; Thomas E. Exner
In this article, we study hybrid Particle Swarm Optimization (PSO) algorithms for continuous optimization. The algorithms\\u000a combine a PSO algorithm with either the Nelder-Mead-Simplex or Powell�s Direction-Set local search methods. Local search is\\u000a applied each time the PSO part meets some convergence criterion. Our experimental results for test functions with up to 100\\u000a dimensions indicate that the usage of the
Optimization of dual-band meander-line polarizer plates using a hybrid evolutionary programming algorithm
Ahmad Hoorfar; Rensheng Sun
We have applied an evolutionary programming (EP) algorithm with a hybrid mutation operator for optimization of dual-band linear polarization (LP) to circular polarization (CP) and LP (V to H) rotation meander-line polarizer plates. This EP algorithm, which was previously introduced in Hoorfar et al. (2000) for antenna problems, uses a combination of the Cauchy and Gaussian mutation operators in order
Effective hybrid evolutionary computational algorithms for global optimization and applied to construct prion AGAAAAGA fibril models
Zhang, Jiapu
Evolutionary algorithms are parallel computing algorithms and simulated annealing algorithm is a sequential computing algorithm. This paper inserts simulated annealing into evolutionary computations and successful developed a hybrid Self-Adaptive Evolutionary Strategy $\\mu+\\lambda$ method and a hybrid Self-Adaptive Classical Evolutionary Programming method. Numerical results on more than 40 benchmark test problems of global optimization show that the hybrid methods presented in this paper are very effective. Lennard-Jones potential energy minimization is another benchmark for testing new global optimization algorithms. It is studied through the amyloid fibril constructions by this paper. To date, there is little molecular structural data available on the AGAAAAGA palindrome in the hydrophobic region (113-120) of prion proteins.This region belongs to the N-terminal unstructured region (1-123) of prion proteins, the structure of which has proved hard to determine using NMR spectroscopy or X-ray crystallography ...
A Hybrid Particle Swarm Optimization Algorithm for Predicting the Chaotic Time Series
Wei Liu; Kejun Wang; Bing Sun; Keyong Shao
A novel hybrid particle swarm optimization (HPSO) is proposed, which the gradient descent learning algorithm is combined with modified particle swarm optimization (MPSO). Firstly, the MPSO was determined by linearly decreasing inertia weight and constriction factor weight to speed up global search, also crossover and mutation operation was embedded to avoid the common defect of premature convergence. Furthermore, gradient descent
A genetic algorithm and a particle swarm optimizer hybridized with Nelder�Mead simplex search
Shu-Kai S. Fan; Yun-Chia Liang; Erwie Zahara
This paper integrates Nelder�Mead simplex search method (NM) with genetic algorithm (GA) and particle swarm optimization (PSO), respectively, in an attempt to locate the global optimal solutions for the nonlinear continuous variable functions mainly focusing on response surface methodology (RSM). Both the hybrid NM�GA and NM�PSO algorithms incorporate concepts from the NM, GA or PSO, which are readily to implement
Multimodal Function Optimizing by a New Hybrid Nonlinear Simplex Search and Particle Swarm Algorithm
Fang Wang; Yuhui Qiu; Naiqin Feng
\\u000a A new hybrid Particle Swarm Optimization (PSO) algorithm is proposed in this paper based on the Nonlinear Simplex Search (NSS)\\u000a method for multimodal function optimizing tasks. At late stage of PSO process, when the most promising regions of solutions\\u000a are fixed, the algorithm isolates particles that fly very close to the extrema and applies the NSS method to them to
An Effective Hybrid Algorithm Based on Simplex Search and Differential Evolution for Global Optimization
Ye Xu; Ling Wang; Lingpo Li
\\u000a In this paper, an effective hybrid NM-DE algorithm is proposed for global optimization by merging the searching mechanisms\\u000a of Nelder-Mead (NM) simplex method and differential evolution (DE). First a reasonable framework is proposed to hybridize\\u000a the NM simplex-based geometric search and the DE-based evolutionary search. Second, the NM simplex search is modified to further\\u000a improve the quality of solutions obtained
Improved Fractal Space Filling Curves Hybrid Optimization Algorithm for Vehicle Routing Problem
Yue, Yi-xiang; Zhang, Tong; Yue, Qun-xing
Vehicle Routing Problem (VRP) is one of the key issues in optimization of modern logistics system. In this paper, a modified VRP model with hard time window is established and a Hybrid Optimization Algorithm (HOA) based on Fractal Space Filling Curves (SFC) method and Genetic Algorithm (GA) is introduced. By incorporating the proposed algorithm, SFC method can find an initial and feasible solution very fast; GA is used to improve the initial solution. Thereafter, experimental software was developed and a large number of experimental computations from Solomon's benchmark have been studied. The experimental results demonstrate the feasibility and effectiveness of the HOA. PMID:26167171
A hybrid optimization technique coupling an evolutionary and a local search algorithm
NASA Astrophysics Data System (ADS)
Kelner, Vincent; Capitanescu, Florin; Leonard, Olivier; Wehenkel, Louis
Evolutionary algorithms are robust and powerful global optimization techniques for solving large-scale problems that have many local optima. However, they require high CPU times, and they are very poor in terms of convergence performance. On the other hand, local search algorithms can converge in a few iterations but lack a global perspective. The combination of global and local search procedures should offer the advantages of both optimization methods while offsetting their disadvantages. This paper proposes a new hybrid optimization technique that merges a genetic algorithm with a local search strategy based on the interior point method. The efficiency of this hybrid approach is demonstrated by solving a constrained multi-objective mathematical test-case.
An integrated ant colony optimization algorithm for the hybrid flow shop scheduling problem
Safa Khalouli; Fatima Ghedjati; Abdelaziz Hamzaoui
This study addresses the multistage hybrid flow shop scheduling problem. The objective of scheduling is to assign each operation to a machine out of the set of eligible machines and to determine the processing operation sequences on the machines so that the makespan is minimized. Hence to solve this NP-hard problem, an integrated ant colony optimization algorithm is proposed. To
An effective hybrid firefly algorithm with harmony search for global numerical optimization.
Guo, Lihong; Wang, Gai-Ge; Wang, Heqi; Wang, Dinan
A hybrid metaheuristic approach by hybridizing harmony search (HS) and firefly algorithm (FA), namely, HS/FA, is proposed to solve function optimization. In HS/FA, the exploration of HS and the exploitation of FA are fully exerted, so HS/FA has a faster convergence speed than HS and FA. Also, top fireflies scheme is introduced to reduce running time, and HS is utilized to mutate between fireflies when updating fireflies. The HS/FA method is verified by various benchmarks. From the experiments, the implementation of HS/FA is better than the standard FA and other eight optimization methods. PMID:24348137
Optimal design of DFG-based wavelength conversion based on hybrid genetic algorithm.
Liu, Xueming; Li, Yanhe
A hybrid genetic algorithm (GA) is proposed. Simulating two test functions shows that the proposed GA can effectively solve the multimodal optimization problems, and the three movies demonstrate the detailed procedure of each generation. The conversion efficiency and bandwidth, based on quasi-phase-matching (QPM) difference frequency generation (DFG), are optimized by the matrix operator and our GA. Optimized examples for five-, six- and seven-segment QPM gratings are given, respectively. The optimal results show that adding the segment number of QPM can obviously broaden the conversion bandwidth, which is sensitive to the fluctuation of bandwidth and the variation of QPM grating period. PMID:19466046
No-idle permutation flow shop scheduling based on a hybrid discrete particle swarm optimization algorithm
Quan-Ke Pan; Ling Wang
A novel hybrid discrete particle swarm optimization (HDPSO) algorithm is proposed in this paper to solve the no-idle permutation\\u000a flow shop scheduling problems with the criterion to minimize the maximum completion time (makespan). Firstly, two simple approaches\\u000a are presented to calculate the makespan of a job permutation. Secondly, a speed-up method is proposed to evaluate the whole\\u000a insert neighborhood of
Identification of metastable ultrasmall titanium oxide clusters using a hybrid optimization algorithm
NASA Astrophysics Data System (ADS)
Inclan, Eric; Geohegan, David; Yoon, Mina
Nanostructured TiO2 materials have interesting properties that are highly relevant to energy and device applications. However, precise control of their morphologies and characterization are still a grand challenge in the field. Using a hybrid optimization algorithm we theoretically explored configuration spaces of energetically metastable TiO2 nanostructures. Our approach is to minimize the total energy of TiO2 clusters in order to identify the structural characteristics and energy landscape of plausible (TiO2)n (n = 1-100). The hybrid algorithm includes a modified differential evolution algorithm, a permutation operator to perform global optimization on a set of randomly generated structures, and then structure refinement using a BFGS Quasi-Newton algorithm. The results were compared against known physical structures and numerical results in the literature as well as our experimentally synthesized structures. Although the global minimum became more computationally expensive to locate with increasing number of TiO2 units, the optimizer successfully identified numerous plausible structures along a range of energies close to the global minimum energy structure for all clusters in the given range. This work is supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division.
Ivan Arsie; Gianfranco Rizzo; Marco Sorrentino
ON THE USE OF GENETIC ALGORITHM TO OPTIMIZE THE ON-BOARD ENERGY MANAGEMENT OF A HYBRID SOLAR VEHICLE � This paper deals with the development of a prototype of Hybrid Solar Vehicle (HSV) with series structure. This activity has been also conducted in the framework of the EU funded Leonardo project \\
A new proposal to hybridize the Nelder-Mead method to a differential evolution algorithm for constrained optimization
Adriana Menchaca-Mendez; Carlos A. Coello Coello
In this paper, we propose a new selection criterion for candidate solutions to a constrained optimization problem. Such a selection mechanism is incorporated into a differential evolution (DE) algorithm. This DE approach is then hybridized with an operator based on the Nelder-Mead method, whose aim is to speed up convergence towards good solutions. The proposed approach is called ldquoHybrid of
A self adaptive hybrid enhanced artificial bee colony algorithm for continuous optimization problems.
Shan, Hai; Yasuda, Toshiyuki; Ohkura, Kazuhiro
The artificial bee colony (ABC) algorithm is one of popular swarm intelligence algorithms that inspired by the foraging behavior of honeybee colonies. To improve the convergence ability, search speed of finding the best solution and control the balance between exploration and exploitation using this approach, we propose a self adaptive hybrid enhanced ABC algorithm in this paper. To evaluate the performance of standard ABC, best-so-far ABC (BsfABC), incremental ABC (IABC), and the proposed ABC algorithms, we implemented numerical optimization problems based on the IEEE Congress on Evolutionary Computation (CEC) 2014 test suite. Our experimental results show the comparative performance of standard ABC, BsfABC, IABC, and the proposed ABC algorithms. According to the results, we conclude that the proposed ABC algorithm is competitive to those state-of-the-art modified ABC algorithms such as BsfABC and IABC algorithms based on the benchmark problems defined by CEC 2014 test suite with dimension sizes of 10, 30, and 50, respectively. PMID:25982071
Hybrid Particle Swarm Optimization: An examination of the inuence of iterative improvement algorithms on its behavior
Jens Gimmler; Thomas Stutzle; Thomas E. Exner
In this article, we study a Hybrid Particle Swarm Opti- mization algorithm (HPSO) that combines a Particle Swarm Optimiza- tion (PSO) algorithm with the Nelder-Mead-Simplex-method (NMS) and with Powell's Direction-Set-Method (PDS). While few publications have shown that the inclusion of NMS into a PSO algorithm can improve per- formance, no careful studies of the behavior of the resulting hybrid algo-
Genetic and Nelder-Mead algorithms hybridized for a more accurate global optimization of continuous multiminima functions
Rachid Chelouah; Patrick Siarry
A hybrid method combining two algorithms is proposed for the global optimization of multiminima functions. To localize a �promising area�, likely to contain a global minimum, it is necessary to well �explore� the whole search domain. When a promising area is detected, the appropriate tools must be used to �exploit� this area and obtain the optimum as accurately and quickly
Model-based Layer Estimation using a Hybrid Genetic/Gradient Search Optimization Algorithm
Chambers, D; Lehman, S; Dowla, F
A particle swarm optimization (PSO) algorithm is combined with a gradient search method in a model-based approach for extracting interface positions in a one-dimensional multilayer structure from acoustic or radar reflections. The basic approach is to predict the reflection measurement using a simulation of one-dimensional wave propagation in a multi-layer, evaluate the error between prediction and measurement, and then update the simulation parameters to minimize the error. Gradient search methods alone fail due to the number of local minima in the error surface close to the desired global minimum. The PSO approach avoids this problem by randomly sampling the region of the error surface around the global minimum, but at the cost of a large number of evaluations of the simulator. The hybrid approach uses the PSO at the beginning to locate the general area around the global minimum then switches to the gradient search method to zero in on it. Examples of the algorithm applied to the detection of interior walls of a building from reflected ultra-wideband radar signals are shown. Other possible applications are optical inspection of coatings and ultrasonic measurement of multilayer structures.
HOPNET: A hybrid ant colony optimization routing algorithm for mobile ad hoc network
Jianping Wang; Eseosa Osagie; Parimala Thulasiraman; Ruppa K. Thulasiram
Mobile ad hoc network (MANET) is a group of mobile nodes which communicates with each other without any supporting infrastructure. Routing in MANET is extremely challenging because of MANETs dynamic features, its limited bandwidth and power energy. Nature-inspired algorithms (swarm intelligence) such as ant colony optimization (ACO) algorithms have shown to be a good technique for developing routing algorithms for
A hybrid discrete particle swarm optimization algorithm for the no-wait flow shop scheduling problem with makespan criterion
Quan-Ke Pan; Ling Wang; M. Fatih Tasgetiren; Bao-Hua Zhao
This paper proposes a novel hybrid discrete particle swarm optimization (HDPSO) algorithm to solve the no-wait flow shop scheduling\\u000a problems with the criterion to minimize the maximum completion time (makespan). Firstly, a simple approach is presented in\\u000a the paper to calculate the makespan of a job permutation. Secondly, a speed-up method is proposed to evaluate the similar\\u000a insert neighborhood solution.
On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems
M. Senthil Arumugam; M. V. C. Rao
This paper presents an alternative and efficient method for solving the optimal control of single-stage hybrid manufacturing systems which are composed with two different cat- egories: continuous dynamics and discrete dynamics. Three different inertia weights, a constant inertia weight (CIW), time-varying inertia weight (TVIW), and global-local best inertia weight (GLbestIW), are considered with the particle swarm optimization (PSO) algorithm to
DESIGN OPTIMIZATION OF A BOW-TIE ANTENNA FOR 2.45 GHz RFID READERS USING A HYBRID BSO- NM ALGORITHM
K. R. Mahmoud
Recently the Bacterial foraging optimization algorithm (BFA) has attracted a lot of attention as a high-performance optimizer. This paper presents a hybrid approach involving Bacterial Swarm Optimization (BSO) and Nelder-Mead (NM) algorithm. The proposed algorithm is used to design a bow-tie antenna for 2.45GHz Radio Frequency Identiflcation (RFID) readers. The antenna is analyzed completely using Method of Moments (MoM), then
An Hybrid Fuzzy Variable Neighborhood Particle Swarm Optimization Algorithm for Solving Quadratic Assignment Problems
Hongbo Liu; Ajith Abraham
Recently, Particle Swarm Optimization (PSO) algorithm has exhibited good performance across a wide range of application problems. A quick review of the literature reveals that research for solving the Quadratic Assignment Problem (QAP) using PSO approach has not much been investigated. In this paper, we design a hy- brid meta-heuristic fuzzy scheme, called as variable neighborhood fuzzy particle swarm algorithm
Optimization by hybridization of a genetic algorithm with constraint satisfaction techniques
Paris-Sud XI, Universit� de
an optimization problem consists in exploring a search space to maximize a given objective function. The relative algorithms [l]are well suited to the quick and globatl exploration of a large search space to optimize any are higly con- strained and have large search spaces. These two features exclude the direct and naive use
Optimization matching of wheel drive hydraulic hybrid vehicle components based on adaptive simulated annealing genetic algorithm
Wang Xin; Jiang Jihai
Compared with the traditional vehicle, a wheel drive hydraulic hybrid vehicle (WDHHV) based on the technique of hydrostatic secondary regulation has the advantages of improved fuel consumption, traction performance and active stability. The parameters of the key components in its drivetrain such as hydraulic pump\\/motors, accumulators, etc. determine the overall efficiency and performance of the vehicle. To optimize the matching
Biomarker Discovery Based on Hybrid Optimization Algorithm and Artificial Neural Networks on Microarray Data for Cancer Classification
Moteghaed, Niloofar Yousefi; Maghooli, Keivan; Pirhadi, Shiva; Garshasbi, Masoud
The improvement of high-through-put gene profiling based microarrays technology has provided monitoring the expression value of thousands of genes simultaneously. Detailed examination of changes in expression levels of genes can help physicians to have efficient diagnosing, classification of tumors and cancer's types as well as effective treatments. Finding genes that can classify the group of cancers correctly based on hybrid optimization algorithms is the main purpose of this paper. In this paper, a hybrid particle swarm optimization and genetic algorithm method are used for gene selection and also artificial neural network (ANN) is adopted as the classifier. In this work, we have improved the ability of the algorithm for the classification problem by finding small group of biomarkers and also best parameters of the classifier. The proposed approach is tested on three benchmark gene expression data sets: Blood (acute myeloid leukemia, acute lymphoblastic leukemia), colon and breast datasets. We used 10-fold cross-validation to achieve accuracy and also decision tree algorithm to find the relation between the biomarkers for biological point of view. To test the ability of the trained ANN models to categorize the cancers, we analyzed additional blinded samples that were not previously used for the training procedure. Experimental results show that the proposed method can reduce the dimension of the data set and confirm the most informative gene subset and improve classification accuracy with best parameters based on datasets. PMID:26120567
Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining
Salehi, Mojtaba
Optimization of process planning is considered as the key technology for computer-aided process planning which is a rather complex and difficult procedure. A good process plan of a part is built up based on two elements: (1) the optimized sequence of the operations of the part; and (2) the optimized selection of the machine, cutting tool and Tool Access Direction (TAD) for each operation. In the present work, the process planning is divided into preliminary planning, and secondary/detailed planning. In the preliminary stage, based on the analysis of order and clustering constraints as a compulsive constraint aggregation in operation sequencing and using an intelligent searching strategy, the feasible sequences are generated. Then, in the detailed planning stage, using the genetic algorithm which prunes the initial feasible sequences, the optimized operation sequence and the optimized selection of the machine, cutting tool and TAD for each operation based on optimization constraints as an additive constraint aggregation are obtained. The main contribution of this work is the optimization of sequence of the operations of the part, and optimization of machine selection, cutting tool and TAD for each operation using the intelligent search and genetic algorithm simultaneously. PMID:21845020
Optimization and Improvement in Robot-Based Assembly Line System by Hybrid Genetic Algorithm
NASA Astrophysics Data System (ADS)
Lin, Lin; Gen, Mitsuo; Gao, Jie
In the real world, there are a lot of scenes from which the product is made by using the robot, which needs different assembly times to perform a given task, because of its capabilities and specialization. For a robotic assembly line balancing (rALB) problem, a set of tasks have to be assigned to stations, and each station needs to select one robot to process the assigned tasks. In this paper, we propose a hybrid genetic algorithm (hGA) for solving this problem. In the hGA, we use new representation method. Advanced genetic operators adapted to the specific chromosome structure and the characteristics of the rALB problem are used. In order to strengthen the search ability, a local search procedure is integrated under the framework the genetic algorithm. Some practical test instances demonstrate the effectiveness and efficiency of the proposed algorithm.
A hybrid Particle Swarm Optimization - Simplex algorithm (PSOS) for structural damage identification
O. Begambre; Jos� Elias Laier
This study proposes a new PSOS-model based damage identification procedure using frequency domain data. The formulation of the objective function for the minimization problem is based on the Frequency Response Functions (FRFs) of the system. A novel strategy for the control of the Particle Swarm Optimization (PSO) parameters based on the Nelder�Mead algorithm (Simplex method) is presented; consequently, the convergence
A Hybrid Discrete Particle Swarm Optimization Algorithm to Solve Flow Shop Scheduling Problems
S. Chandrasekaran; S. G. Ponnambalam; R. K. Suresh; N. Vijayakumar
This paper presents a method of applying particle swarm optimization (PSO) algorithm to a flow shop scheduling problem. Permutation encoding of job indices is used to represent particles. One particle of the initial swarm is generated using NEH heuristic (M. Nawaz, Jr., 1995) and the remaining particles are generated randomly. A continuous swap mechanism is used to improve the performance
Optimal seismic design of reinforced concrete structures under time-history earthquake loads using an intelligent hybrid algorithm
NASA Astrophysics Data System (ADS)
Gharehbaghi, Sadjad; Khatibinia, Mohsen
A reliable seismic-resistant design of structures is achieved in accordance with the seismic design codes by designing structures under seven or more pairs of earthquake records. Based on the recommendations of seismic design codes, the average time-history responses (ATHR) of structure is required. This paper focuses on the optimal seismic design of reinforced concrete (RC) structures against ten earthquake records using a hybrid of particle swarm optimization algorithm and an intelligent regression model (IRM). In order to reduce the computational time of optimization procedure due to the computational efforts of time-history analyses, IRM is proposed to accurately predict ATHR of structures. The proposed IRM consists of the combination of the subtractive algorithm (SA), K-means clustering approach and wavelet weighted least squares support vector machine (WWLS-SVM). To predict ATHR of structures, first, the input-output samples of structures are classified by SA and K-means clustering approach. Then, WWLS-SVM is trained with few samples and high accuracy for each cluster. 9- and 18-storey RC frames are designed optimally to illustrate the effectiveness and practicality of the proposed IRM. The numerical results demonstrate the efficiency and computational advantages of IRM for optimal design of structures subjected to time-history earthquake loads.
A Hybrid Particle Swarm Optimization Algorithm Based on Nonlinear Simplex Method and Tabu Search
Zhanchao Li; Dongjian Zheng; Huijing Hou
\\u000a Particle swarm optimization (PSO) algorithm is an intelligent search method based on swarm intelligence. It has been widely\\u000a used in many fields because of its conciseness and easy implementation. But it is also easy to be plunged into local solution\\u000a and its later convergence speed is very slow. In order to increase its convergence speed, nonlinear simplex method (NSM) is
Efficient Hybrid Grid Synthesis Method Based on Genetic Algorithm for Power/Ground Network Optimization with Dynamic Signal Consideration
NASA Astrophysics Data System (ADS)
Yang, Yun; Kimura, Shinji
This paper proposes an efficient design algorithm for power/ground (P/G) network synthesis with dynamic signal consideration, which is mainly caused by Ldi/dt noise and Cdv/dt decoupling capacitance (DECAP) current in the distribution network. To deal with the nonlinear global optimization under synthesis constraints directly, the genetic algorithm (GA) is introduced. The proposed GA-based synthesis method can avoid the linear transformation loss and the restraint condition complexity in current SLP, SQP, ICG, and random-walk methods. In the proposed Hybrid Grid Synthesis algorithm, the dynamic signal is simulated in the gene disturbance process, and Trapezoidal Modified Euler (TME) method is introduced to realize the precise dynamic time step process. We also use a hybrid-SLP method to reduce the genetic execute time and increase the network synthesis efficiency. Experimental results on given power distribution network show the reduction on layout area and execution time compared with current P/G network synthesis methods.
Multi-agent based hybrid evolutionary algorithm
Xinyan Gan; Jianguo Zheng
In this paper, we have discussed the multi-quantum evolutionary algorithm and simulated annealing algorithm for parallel search of the combination of single-point serial search. From the methodological point of view, it can largely enhance the intelligence of the algorithm. We also introduce the quantum mutation operator into multi-agent quantum evolutionary algorithm to achieve hybrid optimization. I. INTRODUCTION Evolutionary Algorithms (EA)
Efficient use of hybrid Genetic Algorithms in the gain optimization of distributed Raman amplifiers.
Neto, B; Teixeira, A L J; Wada, N; Andr�, P S
In this paper, we propose an efficient and accurate method that combines the Genetic Algorithm (GA) with the Nelder-Mead method in order to obtain the gain optimization of distributed Raman amplifiers. By using these two methods together, the advantages of both are combined: the convergence of the GA and the high accuracy of the Nelder-Mead. To enhance the convergence of the GA, several features were examined and correlated with fitting errors. It is also shown that when the right moment to switch between methods is chosen, the computation time can be reduced by a factor of two. PMID:19551045
Frequency modulation sound parameter identification using novel hybrid evolutionary algorithms
M. T. Vakil Baghmisheh; M. Alinia Ahandani; M. Talebi
The frequency modulation sound parameter identification is a complex multimodal optimization problem. In this paper, we proposed four evolutionary hybrid algorithms to solve this problem. First we combine genetic algorithm (GA) and queen-bee algorithm (QB) with a random optimization method (RO) and generate memetic and QB-memetic hybrid algorithms, respectively; then modified Nelder-Mead simplex algorithm (MNM) combine with particle swarm optimization
PSOVina: The hybrid particle swarm optimization algorithm for protein-ligand docking.
Ng, Marcus C K; Fong, Simon; Siu, Shirley W I
Protein-ligand docking is an essential step in modern drug discovery process. The challenge here is to accurately predict and efficiently optimize the position and orientation of ligands in the binding pocket of a target protein. In this paper, we present a new method called PSOVina which combined the particle swarm optimization (PSO) algorithm with the efficient Broyden-Fletcher-Goldfarb-Shannon (BFGS) local search method adopted in AutoDock Vina to tackle the conformational search problem in docking. Using a diverse data set of 201 protein-ligand complexes from the PDBbind database and a full set of ligands and decoys for four representative targets from the directory of useful decoys (DUD) virtual screening data set, we assessed the docking performance of PSOVina in comparison to the original Vina program. Our results showed that PSOVina achieves a remarkable execution time reduction of 51-60% without compromising the prediction accuracies in the docking and virtual screening experiments. This improvement in time efficiency makes PSOVina a better choice of a docking tool in large-scale protein-ligand docking applications. Our work lays the foundation for the future development of swarm-based algorithms in molecular docking programs. PSOVina is freely available to non-commercial users at http://cbbio.cis.umac.mo . PMID:25800162
Protein Tertiary Structure Prediction Based on Main Chain Angle Using a Hybrid Bees Colony Optimization Algorithm
NASA Astrophysics Data System (ADS)
Mahmood, Zakaria N.; Mahmuddin, Massudi; Mahmood, Mohammed Nooraldeen
Encoding proteins of amino acid sequence to predict classified into their respective families and subfamilies is important research area. However for a given protein, knowing the exact action whether hormonal, enzymatic, transmembranal or nuclear receptors does not depend solely on amino acid sequence but on the way the amino acid thread folds as well. This study provides a prototype system that able to predict a protein tertiary structure. Several methods are used to develop and evaluate the system to produce better accuracy in protein 3D structure prediction. The Bees Optimization algorithm which inspired from the honey bees food foraging method, is used in the searching phase. In this study, the experiment is conducted on short sequence proteins that have been used by the previous researches using well-known tools. The proposed approach shows a promising result.
A Simple But Effective Evolutionary Algorithm for Complicated Optimization Problems
Xu, Y.G.
A simple but effective evolutionary algorithm is proposed in this paper for solving complicated optimization problems. The new algorithm presents two hybridization operations incorporated with the conventional genetic ...
A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and non-convex economic dispatch problem
Taher Niknam
Economic dispatch (ED) plays an important role in power system operation. ED problem is a non-smooth and non-convex problem when valve-point effects of generation units are taken into account. This paper presents an efficient hybrid evolutionary approach for solving the ED problem considering the valve-point effect. The proposed algorithm combines a fuzzy adaptive particle swarm optimization (FAPSO) algorithm with Nelder�Mead
Hybrid Ant Colony Optimization Using Memetic Algorithm for Traveling Salesman Problem
Haibin Duan; Xiufen Yu
Ant colony optimization was originally presented under the inspiration during collective behavior study results on real ant system, and it has strong robustness and easy to combine with other methods in optimization. Although ant colony optimization for the heuristic solution of hard combinational optimization problems enjoy a rapidly growing popularity, but little research is conducted on the optimum configuration strategy
An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem
Guohui Zhang; Xinyu Shao; Peigen Li; Liang Gao
Flexible job-shop scheduling problem (FJSP) is an extension of the classical job-shop scheduling problem. Although the traditional optimization algorithms could obtain preferable results in solving the mono-objective FJSP. However, they are very difficult to solve multi-objective FJSP very well. In this paper, a particle swarm optimization (PSO) algorithm and a tabu search (TS) algorithm are combined to solve the multi-objective
A Fast and Reliable Hybrid Algorithm for Numerical Nonlinear Global Optimization
Paris-Sud XI, Universit� de
such as Evolutionary Algorithms carry out an efficient exploration of the search- space at low cost, but get often out a fast exploration of the search-space and generally converge toward satisfactory solutions a Dif- ferential Evolution algorithm cooperates with Interval Con- straint Programming. It is reliable
Neural network forecast under the organic hybrid model of genetic algorithm and particle swarm algorithm
Shu-Xia Yang
This paper proposes a organic hybrid model of the genetic algorithm and the particle swarm algorithm firstly, then establishes the multi-factor time series forecasting model, designs the BP neural networks, adopts the organic hybrid model of genetic algorithm and particle swarm algorithm to optimize the weight from the input layer to the hidden layer, the weight from the hidden layer
Optimal design of hybrid RO\\/MSF desalination plants Part I: Modeling and algorithms
A. M. Helal; A. M. El-Nashar; E. Al-Katheeri; S. Al-Malek
Hybridization of seawater reverse osmosis (S WRO), desalting technology and the multi-stage flash (MSF) has been considered to improve the performance of the latter and reduce the cost of desalted water. Coupling of the two processes could be made on different levels of integration and the resulting water cost will depend on the selected configuration: not only the plant configuration
A hybrid symbolic/finite-element algorithm for solving nonlinear optimal control problems
NASA Technical Reports Server (NTRS)
Bless, Robert R.; Hodges, Dewey H.
The general code described is capable of solving difficult nonlinear optimal control problems by using finite elements and a symbolic manipulator. Quick and accurate solutions are obtained with a minimum for user interaction. Since no user programming is required for most problems, there are tremendous savings to be gained in terms of time and money.
A hybrid method combining continuous tabu search and Nelder-Mead simplex algorithms for the global optimization of multiminima functions
Rachid Chelouah; Patrick Siarry
Tabu search (TS) is a metaheuristic, which proved efficient to solve various combinatorial optimization problems. However, few works deal with its application to the global minimization of functions depending on continuous variables. To perform this task, we propose an hybrid method combining tabu search and simplex search (SS). TS allows to cover widely the solution space, to stimulate the search
Hybrid algorithm of minimum relative entropy-particle swarm optimization with adjustment parameters for gas source term identification in atmosphere
NASA Astrophysics Data System (ADS)
Ma, Denglong; Wang, Simin; Zhang, Zaoxiao
In order to identify the source term of gas emission in atmosphere, an improved hybrid algorithm combined with the minimum relative entropy (MRE) and particle swarm optimization (PSO) method was presented. Not only are the estimated source parameters obtained, but also the confidence intervals at some probability levels. If only the source strength was required to be determined, the problem can be viewed as a linear inverse problem directly, which can be solved by original MRE method successfully. When both source strength and location are unknown, the common gas dispersion model should be transformed to be a linear system. Although the transformed linear model has some differences from that in original MRE method, satisfied estimation results were still obtained by adding iteratively adaptive adjustment parameters in the MRE-PSO method. The dependence of the MRE-PSO method on prior information such as lower and upper bound, prior expected values and noises were also discussed. The results showed that the confidence intervals and estimated parameters are influenced little by the prior bounds and expected values, but the errors affect the estimation results greatly. The simulation and experiment verification results showed that the MRE-PSO method is able to identify the source parameters with satisfied results. Finally, the error model was probed and then it was added in the MRE-PSO method. The addition of error model improves the performance of the identification method. Therefore, the MRE-PSO method with adjustment parameters proposed in this paper is a potential good method to resolve inverse problem in atmosphere environment.
Parallel Hybrid Particle Swarm Optimization and Applications in Geotechnical Engineering
Youliang Zhang; Domenico Gallipoli; Charles Augarde
A novel parallel hybrid particle swarm optimization algorithm named hmPSO is presented. The new algorithm combines particle\\u000a swarm optimization (PSO) with a local search method which aims to accelerate the rate of convergence. The PSO provides initial\\u000a guesses to the local search method and the local search accelerates PSO with its solutions. The hybrid global optimization\\u000a algorithm adjusts its searching
A robust hybrid spectral estimation algorithm for SAR imaging
Jian Li; P. Stoica; Zhaoqiang Bi; Renbiao Wu; E. G. Zelnio
This paper presents a robust and computationally efficient hybrid spectral estimation algorithm, referred to as Hybrid, for synthetic aperture radar (SAR) target feature extraction and image formation. Hybrid first extracts the target features via a relaxation-based optimization approach based on a flexible data model which uses a complex sinusoid with an arbitrary unknown amplitude and a constant phase in cross-range
Application of a hybrid of particle swarm and genetic algorithm for structural damage detection
S. Sandesh; K. Shankar
This study presents a novel optimization algorithm which is a hybrid of particle swarm optimization (PSO) method and genetic algorithm (GA). Using the Ackley and Schwefel multimodal benchmark functions incorporating up to 25 variables, the performance of the hybrid is compared with pure PSO and GA and found to be far superior in convergence and accuracy. The hybrid algorithm is
A hybrid genetic algorithm for resolving closely spaced objects
NASA Technical Reports Server (NTRS)
Abbott, R. J.; Lillo, W. E.; Schulenburg, N.
A hybrid genetic algorithm is described for performing the difficult optimization task of resolving closely spaced objects appearing in space based and ground based surveillance data. This application of genetic algorithms is unusual in that it uses a powerful domain-specific operation as a genetic operator. Results of applying the algorithm to real data from telescopic observations of a star field are presented.
A hybrid optimization methods for nonlinear programming
Erwie Zahara; Yi-Tung Kao; Chia-Hsin Hu
Nonlinear programming models often arise in science and engineering. A nonlinear programming model consists of the optimization of a function subject to constraints, in which both the function and constraints may be nonlinear. This paper proposes the hybrid NM-PSO algorithm, which is based on nelder-mead (NM) simplex search method and particle swarm optimization (PSO), for solving nonlinear programming models. NM-PSO
Firefly Algorithms for Multimodal Optimization
Yang, Xin-She
Nature-inspired algorithms are among the most powerful algorithms for optimization. This paper intends to provide a detailed description of a new Firefly Algorithm (FA) for multimodal optimization applications. We will compare the proposed firefly algorithm with other metaheuristic algorithms such as particle swarm optimization (PSO). Simulations and results indicate that the proposed firefly algorithm is superior to existing metaheuristic algorithms. Finally we will discuss its applications and implications for further research.
Genetic algorithm and particle swarm optimization combined with Powell method
NASA Astrophysics Data System (ADS)
Bento, David; Pinho, Diana; Pereira, Ana I.; Lima, Rui
In recent years, the population algorithms are becoming increasingly robust and easy to use, based on Darwin's Theory of Evolution, perform a search for the best solution around a population that will progress according to several generations. This paper present variants of hybrid genetic algorithm - Genetic Algorithm and a bio-inspired hybrid algorithm - Particle Swarm Optimization, both combined with the local method - Powell Method. The developed methods were tested with twelve test functions from unconstrained optimization context.
Modeling Hybrid Genetic Algorithms Darrell Whitley
Whitley, Darrell
Modeling Hybrid Genetic Algorithms Darrell Whitley Computer Science Department, Colorado State University, Fort Collins, CO 80523 firstname.lastname@example.com 1 INTRODUCTION A ``hybrid genetic algorithm'' combines local search with a more traditional genetic algorithm. The most common form of hybrid genetic
Hybrid Algorithms for Fuzzy Reverse Supply Chain Network Design
Che, Z. H.; Chiang, Tzu-An; Kuo, Y. C.
In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods. PMID:24892057
Hybrid cars, optimization and control
A. Kleimaier; D. Schr der
The paper presents new optimization results for the design and control of a hybrid vehicle. The powertrain consists of a combustion engine and an electrical drive, combined with a continuously variable transmission gear. For simulation and analysis, the optimal control of the powertrain is calculated directly by using of the optimal control theory. The objective is minimum fuel consumption. From
Hybrid simplex search and particle swarm optimization for the global optimization of multimodal functions
Shu-kai S. Fan; Yun-chia Liang; Erwie Zahara
This article proposes the hybrid Nelder�Mead (NM)�Particle Swarm Optimization (PSO) algorithm based on the NM simplex search method and PSO for the optimization of multimodal functions. The hybrid NM�PSO algorithm is very easy to implement, in practice, since it does not require gradient computation. This hybrid procedure performed the exploration with PSO and the exploitation with the NM simplex search
An Algorithmic Framework for Multiobjective Optimization
Ganesan, T.; Elamvazuthi, I.; Shaari, Ku Zilati Ku; Vasant, P.
Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization. PMID:24470795
An algorithmic framework for multiobjective optimization.
Ganesan, T; Elamvazuthi, I; Shaari, Ku Zilati Ku; Vasant, P
Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization. PMID:24470795
A particle swarm optimization algorithm based on orthogonal design
Jie Yang; Abdesselam Bouzerdoum; Son Lam Phung
The last decade has witnessed a great interest in using evolutionary algorithms, such as genetic algorithms, evolutionary strategies and particle swarm optimization (PSO), for multivariate optimization. This paper presents a hybrid algorithm for searching a complex domain space, by combining the PSO and orthogonal design. In the standard PSO, each particle focuses only on the error propagated back from the
A Hybrid Classification Algorithm Evaluated on Medical Data
Ioannis Michelakos; Elpiniki Papageorgiou; Michael Vasilakopoulos
Ant colony optimization algorithms have been applied successfully to data mining classification problems. Recently, an improved version of cAnt-Miner (Ant-Miner coping with continuous attributes), called cAnt-Miner2, has been introduced for mining classification rules. In this paper, a hybrid algorithm is presented, combining the cAnt-Miner2 and the mRMR feature selection algorithms. The proposed algorithm was experimentally compared to cAnt-Miner2, using some
Foundations Algorithm Components Numerical Optimization Genetic Programming Genetic Algorithms
Kjellstr�m, Hedvig
Foundations Algorithm Components Numerical Optimization Genetic Programming Genetic Algorithms Foundations Algorithm Components Numerical Optimization Genetic Programming 1 Foundations 2 Algorithm Programming Example Foundations Algorithm Components Numerical Optimization Genetic Programming Genetic
Foundations Algorithm Components Numerical Optimization Genetic Programming Genetic Algorithms
Kjellstr�m, Hedvig
Foundations Algorithm Components Numerical Optimization Genetic Programming Genetic Algorithms #12;Foundations Algorithm Components Numerical Optimization Genetic Programming 1 Foundations 2 Algorithm Programming Example #12;Foundations Algorithm Components Numerical Optimization Genetic Programming Genetic
A hybrid multi-path ant QoS routing algorithm for MANETs
Radwa Attia; R. Rizk; Mahmoud Mariee
Supporting multimedia for Mobile ad hoc networks (MANETs) is an important issue. This paper presents two routing algorithms in MANETs inspired by the ant colony optimization routing algorithms. The first algorithm is a Hybrid Multi-Ant (HMAnt) routing algorithm. It is a hybrid since it combines reactive path establishment with proactive path maintenance. It supports multi-path while maintaining an acceptable level
Evolving the Structure of the Particle Swarm Optimization Algorithms
Laura Diosan; Mihai Oltean
A new model for evolving the structure of a Particle Swarm Optimization (PSO) algorithm is proposed in this paper. The model is a hybrid technique that combines a Genetic Algorithm (GA) and a PSO algorithm. Each GA chromosome is an array encoding a meaning for updating the particles of the PSO algorithm. The evolved PSO algo- rithm is compared to
An improved genetic algorithm with variable population-size and a PSO-GA based hybrid evolutionary algorithm
X. H. Shi; L. M. Wan; H. P. Lee; X. W. Yang; L. M. Wang; Y. C. Liang
This paper presents an improved genetic algorithm with variable population-size (VPGA) inspired by the natural features of the variable size of the population. Based on the VPGA and the particle swarm optimization (PSO) algorithms, this paper also proposes a novel hybrid approach called PSO-GA based hybrid evolutionary algorithm (PGBHEA). Simulations show that both VPGA and PGBHEA are effective for the
Hybrid Ant Algorithm and Applications for Vehicle Routing Problem
NASA Astrophysics Data System (ADS)
Xiao, Zhang; Jiang-qing, Wang
Ant colony optimization (ACO) is a metaheuristic method that inspired by the behavior of real ant colonies. ACO has been successfully applied to several combinatorial optimization problems, but it has some short-comings like its slow computing speed and local-convergence. For solving Vehicle Routing Problem, we proposed Hybrid Ant Algorithm (HAA) in order to improve both the performance of the algorithm and the quality of solutions. The proposed algorithm took the advantages of Nearest Neighbor (NN) heuristic and ACO for solving VRP, it also expanded the scope of solution space and improves the global ability of the algorithm through importing mutation operation, combining 2-opt heuristics and adjusting the configuration of parameters dynamically. Computational results indicate that the hybrid ant algorithm can get optimal resolution of VRP effectively.
A hybrid immune PSO for constrained optimization problems
Aijia Ouyang
Precise Algorithms combining evolutionary algorithms and constraint-handling techniques have shown to be effective to solve constrained optimization problems during the past decade. This paper presents a hybrid immune PSO (HIA-PSO) algorithm with a feasibility-based rule which is employed in this paper to handle constraints in solving global nonlinear constrained optimization problems, and Nelder-Mead simplex search method is used to improve
Ant Algorithms for Discrete Optimization
Ducatelle, Frederick
Ant Algorithms for Discrete Optimization Marco Dorigo and Gianni Di Caro IRIDIA, Universit#19;e, Switzerland email@example.com Abstract This paper overviews recent work on ant algorithms, that is, algorithms for discrete optimization which took inspiration from the observation of ant colonies foraging behavior
Ant Algorithms for Discrete Optimization
Gambardella, Luca Maria
Ant Algorithms for Discrete Optimization Marco Dorigo and Gianni Di Caro IRIDIA, Universit´e Libre, Switzerland firstname.lastname@example.com Abstract This paper overviews recent work on ant algorithms, that is, algorithms for discrete optimization which took inspiration from the observation of ant colonies foraging behavior
Fast Laser Cutting Optimization Algorithm
B. Adelmann; R. Hellmann
To obtain high quality results in laser fusion cutting, generally, a time and cost intensive optimization process has to be run. We report on a fast algorithm to optimize the laser parameters to get a burr free laser cut. The algorithm includes design of experiments and one-factor-at-a-time methods. The algorithm describes the whole optimization from the first to the optimum
Hybrid optimization methods for Full Waveform Inversion
NASA Astrophysics Data System (ADS)
Datta, D.; Sen, M. K.
FWI is slowly becoming the mainstream method to estimate velocity models of the subsurface from seismic data. Typically it makes use of a gradient descent approach in which a model update is computed by back propagating the residual seismograms and cross correlating with the forward propagating wavefields at each grid point in the subsurface model. FWI is a local optimization technique, which requires the starting model to be very close to the true model. Because the objective function is multimodal with many local minima, the requirement of good starting model becomes essential. A starting model is generated using travel time tomography. We propose two hybrid FWI algorithms one of which generates a very good starting model for a conventional FWI and the other, which works with a population of models uses gradient information from multiple starting locations in guiding the search. The first approach uses a sparse parameterization of model space using non-oscillatory splines, whose coeffiencts are estimated using an optimization algorithm like very fast simulated annealing (VFSA) by minimizing the misfit between the observed and synthetic data. The estimated velocity model is then used as a starting model for gradient-based FWI. This is done in the shot domain by converting the end-on marine geometry to a split spread geometry using the principle of reciprocity. The second approach is to uses an alternate global optimization algorithm called particle swarm optimization (PSO) where PSO update rules are applied. However, we employ a new gradient guided PSO that exploits the gradient information as well. This approach avoids the local minima and converges faster than a conventional PSO. We demonstrate our methods with application to 2D marine data sets from offshore India. Each line comprises over 1000 shots; our hybrid methods produce geologically meaningful velocity models fairly rapidly on a GPU cluster. We show that starting with the hybrid model gives a much better velocity model than starting with a simple smooth model.
Optimization of hybrid satellite and constellation design for GEO-belt space situational awareness using genetic algorithms
Eugene Fahnestock; Richard Scott Erwin
Meeting the goals of space situational awareness requires the capabilities of imaging objects in space in the visible light hand at high resolutions and tracking their positions and orbits. This paper summarizes the determination of designs for a hybrid constellation consisting of two types of satellites to provide these capabilities in the vicinity of equatorial GEO. This is cast as
A hybrid fast Hankel transform algorithm has been developed that uses several complementary features of two existing algorithms: Anderson's digital filtering or fast Hankel transform (FHT) algorithm and Chave's quadrature and continued fraction algorithm. A hybrid FHT subprogram ...
A Novel GA-LM Based Hybrid Algorithm
Changsheng Zhang; Jigui Sun; Qiansheng Wang; Zhe Feng
In order to improve the model's learning capability and convergence rate, the GA and ANN are usually combined together. But the current combination ways have the insufficiencies of premature convergence, weak extensive ability etc. To overcome these shortcomings, we propose a new hybrid study algorithm-GALM, which uses the GA and LM in turn to optimize the neural network, we compare
The Rational Hybrid Monte Carlo Algorithm
M. A. Clark
The past few years have seen considerable progress in algorithmic development for the generation of gauge fields including the effects of dynamical fermions. The Rational Hybrid Monte Carlo (RHMC) algorithm, where Hybrid Monte Carlo is performed using a rational approximation in place the usual inverse quark matrix kernel is one of these developments. This algorithm has been found to be extremely beneficial in many areas of lattice QCD (chiral fermions, finite temperature, Wilson fermions etc.). We review the algorithm and some of these benefits, and we compare against other recent algorithm developements. We conclude with an update of the Berlin wall plot comparing costs of all popular fermion formulations.
Optimal control for a parallel hybrid hydraulic excavator using particle swarm optimization.
Wang, Dong-yun; Guan, Chen
Optimal control using particle swarm optimization (PSO) is put forward in a parallel hybrid hydraulic excavator (PHHE). A power-train mathematical model of PHHE is illustrated along with the analysis of components' parameters. Then, the optimal control problem is addressed, and PSO algorithm is introduced to deal with this nonlinear optimal problem which contains lots of inequality/equality constraints. Then, the comparisons between the optimal control and rule-based one are made, and the results show that hybrids with the optimal control would increase fuel economy. Although PSO algorithm is off-line optimization, still it would bring performance benchmark for PHHE and also help have a deep insight into hybrid excavators. PMID:23818832
An Ant Colony System Hybridized with Randomized Algorithm for TSP
Chengming Qi
Ant algorithms are a recently developed, population- based approach which has been successfully applied to several NP-hard combinatorial optimization problems. In this paper, through an analysis of the constructive procedure of the solution in the ant colony system (ACS),we present an ant colony system hybridized with randomized algorithm(RAACS). In RAACS, only partial cities are randomly chosen to compute the state
A Hybrid Admissible Distortion Checking Algorithm for the B-Spline-Based Operational Rate-Distortion Optimal Shape Coding
-Distortion Optimal Shape Coding Zhongyuan Lai, Zhen Zuo, Zhe Wang, and Wenyu Liu, Member, IEEE Dept. of Electron. and Inf. Eng., Huazhong Univ. of Sci. and Technol. Wuhan, China Email: {laizhy, zzhen1990, twolucky.wang. Lett., vol. 14, no. 2, pp: 121-124, Feb. 2007. [3] Z. Lai, Z. Zuo, Z. Wang, and W. Liu, "Accurate
Hybrid particle swarm optimization for hybrid flowshop scheduling problem with maintenance activities.
Li, Jun-qing; Pan, Quan-ke; Mao, Kun
A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and N�ron's benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm. PMID:24883414
A Hybrid Genetic Algorithm for School Timetabling
Peter Wilke; Matthias Gr�bner; Norbert Oster
Hybrid Genetic Algorithms apply so called hybrid or repair operators or include problem specific knowledge about the problem domain in their mutation and crossover operators. These operators use local search to repair or avoid illegal or unsuitable assignments or just to improve the quality of the solutions already found.
Economic Dispatch Using Genetic Algorithm Based Hybrid Approach
Tahir Nadeem Malik; Aftab Ahmad [University of Engineering and Technology, Taxila (Pakistan); Shahab Khushnood [National Power Construction Corporation - NPCC, 9-Shadman II, Lahore -54000 (Pakistan)
Power Economic Dispatch (ED) is vital and essential daily optimization procedure in the system operation. Present day large power generating units with multi-valves steam turbines exhibit a large variation in the input-output characteristic functions, thus non-convexity appears in the characteristic curves. Various mathematical and optimization techniques have been developed, applied to solve economic dispatch (ED) problem. Most of these are calculus-based optimization algorithms that are based on successive linearization and use the first and second order differentiations of objective function and its constraint equations as the search direction. They usually require heat input, power output characteristics of generators to be of monotonically increasing nature or of piecewise linearity. These simplifying assumptions result in an inaccurate dispatch. Genetic algorithms have used to solve the economic dispatch problem independently and in conjunction with other AI tools and mathematical programming approaches. Genetic algorithms have inherent ability to reach the global minimum region of search space in a short time, but then take longer time to converge the solution. GA based hybrid approaches get around this problem and produce encouraging results. This paper presents brief survey on hybrid approaches for economic dispatch, an architecture of extensible computational framework as common environment for conventional, genetic algorithm and hybrid approaches based solution for power economic dispatch, the implementation of three algorithms in the developed framework. The framework tested on standard test systems for its performance evaluation. (authors)
Swarm Optimization Algorithms Incorporating Design Sensitivities
Kazuhiro Izui; Shinji Nishiwaki; Masataka Yoshimura
1. Abstract Swarm algorithms such as Particle Swarm Optimization (PSO) are non-gradient probabilistic optimization algorithms that have been successfully applied to obtain global optimal solutions for complex problems such as multi-peak problems. However these algorithms have not been applied to complicated structural and mechanical optimization problems since local optimization capability is still inferior to general numerical optimization methods. This paper
Fairness in optimal routing algorithms�
Goos, Jeffrey Alan
. Tsei Dr. Pierce E. Cantrell A study of fairness in multiple path optimal routing algorithms is discussed. Fair- ness measures are developed to evaluate multiple path routing in virtual circuit and datagram implementations. Several objective...
Ant Algorithms for Discrete Optimization
Hutter, Frank
Ant Algorithms for Discrete Optimization Marco Dorigo Gianni Di Caro IRIDIA CP 194/6 email@example.com Luca M. Gambardella IDSIA Corso Elvezia 36 CH-6900 Lugano Switzerland email@example.com Keywords ant algorithms, ant colony optimiza- tion, swarm intelligence, metaheuris- tics, natural computation Abstract
Optimizing the specificity of nucleic acid hybridization
Sherry Xi Chen; David Yu Zhang; Peng Yin
The specific hybridization of complementary sequences is an essential property of nucleic acids, enabling diverse biological and biotechnological reactions and functions. However, the specificity of nucleic acid hybridization is compromised for long strands, except near the melting temperature. Here, we analytically derived the thermodynamic properties of a hybridization probe that would enable near-optimal single-base discrimination and perform robustly across diverse
Evolutionary Algorithms and Matroid Optimization Joachim Reichel #
Evolutionary Algorithms and Matroid Optimization Problems Joachim Reichel # Department the performance of evolutionary algorithms on various matroid optimization problems that encompass a vast number, Performance Keywords evolutionary algorithms, matroids, minimum weight basis, matroid intersection, randomized
An extended discrete particle swarm optimization algorithm for the dynamic facility layout problem
Hassan Rezazadeh; Mehdi Ghazanfari; Mohammad Saidi-Mehrabad; Seyed Jafar Sadjadi
We extended an improved version of the discrete particle swarm optimization (DPSO) algorithm proposed by Liao et al. (2007) to solve the dynamic facility layout problem (DFLP). A computational study was performed with the existing heuristic\\u000a algorithms, including the dynamic programming (DP), genetic algorithm (GA), simulated annealing (SA), hybrid ant system (HAS),\\u000a hybrid simulated annealing (SA-EG), hybrid genetic algorithms (NLGA
Optimal brake torque distribution for a four-wheeldrive hybrid electric vehicle stability enhancement
D-H Kim; J-M Kim; S-H Hwang; H-S Kim
Vehicle stability control logic for a four-wheel-drive hybrid electric vehicle is proposed using the regenerative braking of the rear motor and an electrohydraulic brake (EHB). To obtain the optimal brake torque distribution between the regenerative braking and the EHB torque, a genetic algorithm is used. The genetic algorithm calculates the optimal regenerative braking torque and the optimal EHB torque for
Algorithms for bilevel optimization
NASA Technical Reports Server (NTRS)
Alexandrov, Natalia; Dennis, J. E., Jr.
General multilevel nonlinear optimization problems arise in design of complex systems and can be used as a means of regularization for multi-criteria optimization problems. Here, for clarity in displaying our ideas, we restrict ourselves to general bi-level optimization problems, and we present two solution approaches. Both approaches use a trust-region globalization strategy, and they can be easily extended to handle the general multilevel problem. We make no convexity assumptions, but we do assume that the problem has a nondegenerate feasible set. We consider necessary optimality conditions for the bi-level problem formulations and discuss results that can be extended to obtain multilevel optimization formulations with constraints at each level.
Hybrid Genetic Algorithm for Designing Logistics Network, VRP and AGV Problems
Mitsuo Gen; Lin Lin; Jung-Bok Jo
The use of hybrid genetic algorithm (Hybrid GA) in the networks design has been growing the last decades due to the fact that\\u000a many practical networks design problems are NP hard. This paper examines recent developments in the field of evolutionary\\u000a optimization for network design. We combine the various hybrid genetic algorithms to a wide range of practical network problems
About solving hybrid optimal control problems
P. Riedinger; J. Daafouz; C. Iung
The main objective of this paper is to discuss nu- merical difculties in solving hybrid optimal control problems and to propose a multiple phase-multiple shooting formula- tion for hybrid optimal control design. Such a formulation allows to solve directly the problem using nonlinear program- ming techniques. In the case of switched systems, it is shown that the switching rule can
A Particle Swarm Optimization Algorithm with Path Relinking for the Location Routing Problem
Yannis Marinakis; Magdalene Marinaki
This paper introduces a new hybrid algorithmic nature inspired approach based on particle swarm optimization, for solving\\u000a successfully one of the most popular logistics management problems, the location routing problem (LRP). The proposed algorithm\\u000a for the solution of the location routing problem, the hybrid particle swarm optimization (HybPSO-LRP), combines a particle\\u000a swarm optimization (PSO) algorithm, the multiple phase neighborhood search
A hybrid cooperative-comprehensive learning based PSO algorithm for image segmentation using multilevel thresholding
Madhubanti Maitra; Amitava Chatterjee
A novel optimal multilevel thresholding algorithm for histogram-based image segmentation is presented in this paper. The proposed algorithm presents an improved variant of PSO, a relatively recently introduced stochastic optimization strategy. This hybrid approach employs both cooperative learning and comprehensive learning along with some additional modifications. Cooperative learning is employed to overcome the �curse of dimensionality� by decomposing a high-dimensional
Time series prediction with recurrent neural networks using a hybrid PSO-EA algorithm
Xindi Cai; Nian Zhang; Ganesh K. Venayagamoorthy; Donald C. Wunsch I
To predict the 100 missing values from the time series consisting of 5000 data given for the IJCNN 2004 time series prediction competition, we applied an architecture which automates the design of recurrent neural networks using a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of particle swarm optimization (PSO) and evolutionary algorithm (EA).
A hybrid evolutionary algorithm for wheat blending problem.
Li, Xiang; Bonyadi, Mohammad Reza; Michalewicz, Zbigniew; Barone, Luigi
This paper presents a hybrid evolutionary algorithm to deal with the wheat blending problem. The unique constraints of this problem make many existing algorithms fail: either they do not generate acceptable results or they are not able to complete optimization within the required time. The proposed algorithm starts with a filtering process that follows predefined rules to reduce the search space. Then the linear-relaxed version of the problem is solved using a standard linear programming algorithm. The result is used in conjunction with a solution generated by a heuristic method to generate an initial solution. After that, a hybrid of an evolutionary algorithm, a heuristic method, and a linear programming solver is used to improve the quality of the solution. A local search based posttuning method is also incorporated into the algorithm. The proposed algorithm has been tested on artificial test cases and also real data from past years. Results show that the algorithm is able to find quality results in all cases and outperforms the existing method in terms of both quality and speed. PMID:24707222
A hybrid simplex search and particle swarm optimization for unconstrained optimization
Shu-kai S. Fan; Erwie Zahara
This paper proposes the hybrid NM-PSO algorithm based on the Nelder�Mead (NM) simplex search method and particle swarm optimization (PSO) for unconstrained optimization. NM-PSO is very easy to implement in practice since it does not require gradient computation. The modification of both the Nelder�Mead simplex search method and particle swarm optimization intends to produce faster and more accurate convergence. The
A Novel Hybrid Self-Adaptive Bat Algorithm
Fister, Iztok; Brest, Janez
Nature-inspired algorithms attract many researchers worldwide for solving the hardest optimization problems. One of the newest members of this extensive family is the bat algorithm. To date, many variants of this algorithm have emerged for solving continuous as well as combinatorial problems. One of the more promising variants, a self-adaptive bat algorithm, has recently been proposed that enables a self-adaptation of its control parameters. In this paper, we have hybridized this algorithm using different DE strategies and applied these as a local search heuristics for improving the current best solution directing the swarm of a solution towards the better regions within a search space. The results of exhaustive experiments were promising and have encouraged us to invest more efforts into developing in this direction. PMID:25187904
A novel hybrid self-adaptive bat algorithm.
Fister, Iztok; Fong, Simon; Brest, Janez; Fister, Iztok
Nature-inspired algorithms attract many researchers worldwide for solving the hardest optimization problems. One of the newest members of this extensive family is the bat algorithm. To date, many variants of this algorithm have emerged for solving continuous as well as combinatorial problems. One of the more promising variants, a self-adaptive bat algorithm, has recently been proposed that enables a self-adaptation of its control parameters. In this paper, we have hybridized this algorithm using different DE strategies and applied these as a local search heuristics for improving the current best solution directing the swarm of a solution towards the better regions within a search space. The results of exhaustive experiments were promising and have encouraged us to invest more efforts into developing in this direction. PMID:25187904
Size optimization of space trusses using Big Bang�Big Crunch algorithm
A. Kaveh; S. Talatahari
A Hybrid Big Bang�Big Crunch (HBB�BC) optimization algorithm is employed for optimal design of truss structures. HBB�BC is compared to Big Bang�Big Crunch (BB�BC) method and other optimization methods including Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization and Harmony Search. Numerical results demonstrate the efficiency and robustness of the HBB�BC method compared to other heuristic algorithms.
A real-coded genetic algorithm involving a hybrid crossover method for power plant control system design
Kwang Y. Lee; P. S. Mohamed
This paper introduces a new hybrid crossover method for a real-coded genetic algorithm and its application to control system design of a power plant. Determining gains for controllers by using a genetic algorithm method usually involves multiple training stages. This method is not necessarily optimal. This paper applies a hybrid crossover method in a real-coded genetic algorithm to simultaneously find
Constrained Multiobjective Biogeography Optimization Algorithm
Mo, Hongwei; Xu, Zhidan; Xu, Lifang; Wu, Zhou; Ma, Haiping
Multiobjective optimization involves minimizing or maximizing multiple objective functions subject to a set of constraints. In this study, a novel constrained multiobjective biogeography optimization algorithm (CMBOA) is proposed. It is the first biogeography optimization algorithm for constrained multiobjective optimization. In CMBOA, a disturbance migration operator is designed to generate diverse feasible individuals in order to promote the diversity of individuals on Pareto front. Infeasible individuals nearby feasible region are evolved to feasibility by recombining with their nearest nondominated feasible individuals. The convergence of CMBOA is proved by using probability theory. The performance of CMBOA is evaluated on a set of 6 benchmark problems and experimental results show that the CMBOA performs better than or similar to the classical NSGA-II and IS-MOEA. PMID:25006591
Constrained multiobjective biogeography optimization algorithm.
Mo, Hongwei; Xu, Zhidan; Xu, Lifang; Wu, Zhou; Ma, Haiping
Multiobjective optimization involves minimizing or maximizing multiple objective functions subject to a set of constraints. In this study, a novel constrained multiobjective biogeography optimization algorithm (CMBOA) is proposed. It is the first biogeography optimization algorithm for constrained multiobjective optimization. In CMBOA, a disturbance migration operator is designed to generate diverse feasible individuals in order to promote the diversity of individuals on Pareto front. Infeasible individuals nearby feasible region are evolved to feasibility by recombining with their nearest nondominated feasible individuals. The convergence of CMBOA is proved by using probability theory. The performance of CMBOA is evaluated on a set of 6 benchmark problems and experimental results show that the CMBOA performs better than or similar to the classical NSGA-II and IS-MOEA. PMID:25006591
A modified particle swarm optimization algorithm
Junjun Li; Xihuai Wang
A modified particle swarm optimization (PSO) algorithms is proposed. This method integrates the particle swarm optimization with the simulated annealing algorithm. It can solve the problem of local minimum of the particle swarm optimization, and narrow the field of search continually, so it has higher efficiency of search. This algorithm is applied to the function optimization problem and simulation shows
Adve, Raviraj
, Hybrid algo- rithm, non-homogeneous clutter Abstract This paper presents a knowledge based hybrid. In the hybrid algorithm, statistical and non-statistical direct data domain (D3 ) algo- rithms are combined by the efficient STAP algorithm. The performance of the hybrid algo- rithm using D3 � STAP is tested in SIRV
Exchange Rate Forecasting with Hybrid Genetic Algorithms
Jui-Fang Chang
\\u000a In recent years, Artificial Intelligence (AI) methods have proven to be successful tools for forecasting in the sectors of\\u000a business, finance, medical science and engineering. In this study, we employ a Genetic Algorithm (GA) to select the optimal\\u000a variable weights in order to predict exchange rates; subsequently, Genetic Algorithms, Particle Swam Optimization (PSO) and\\u000a Back Propagation Network (BPN) are utilized
A Hybrid Compression Algorithm for Compound Images
R. Ramya; K. Mala
This paper presents a hybrid image coding scheme, based on shape primitives, termed Shape Primitive Extraction and Coding (SPEC). It is required that the compression algorithm should not only achieve high compression ratio, but also low complexity and high visual quality. The segmentation classifies image blocks into picture and text\\/graphics blocks by thresholding the number of colors of each block,
Optimal Control of Hybrid Systems in Air Traffic Applications
NASA Astrophysics Data System (ADS)
Kamgarpour, Maryam
Growing concerns over the scalability of air traffic operations, air transportation fuel emissions and prices, as well as the advent of communication and sensing technologies motivate improvements to the air traffic management system. To address such improvements, in this thesis a hybrid dynamical model as an abstraction of the air traffic system is considered. Wind and hazardous weather impacts are included using a stochastic model. This thesis focuses on the design of algorithms for verification and control of hybrid and stochastic dynamical systems and the application of these algorithms to air traffic management problems. In the deterministic setting, a numerically efficient algorithm for optimal control of hybrid systems is proposed based on extensions of classical optimal control techniques. This algorithm is applied to optimize the trajectory of an Airbus 320 aircraft in the presence of wind and storms. In the stochastic setting, the verification problem of reaching a target set while avoiding obstacles (reach-avoid) is formulated as a two-player game to account for external agents' influence on system dynamics. The solution approach is applied to air traffic conflict prediction in the presence of stochastic wind. Due to the uncertainty in forecasts of the hazardous weather, and hence the unsafe regions of airspace for aircraft flight, the reach-avoid framework is extended to account for stochastic target and safe sets. This methodology is used to maximize the probability of the safety of aircraft paths through hazardous weather. Finally, the problem of modeling and optimization of arrival air traffic and runway configuration in dense airspace subject to stochastic weather data is addressed. This problem is formulated as a hybrid optimal control problem and is solved with a hierarchical approach that decouples safety and performance. As illustrated with this problem, the large scale of air traffic operations motivates future work on the efficient implementation of the proposed algorithms.
Magdon-Ismail, Malik
EMPIRICAL ANALYSIS OF OPTIMIZATION ALGORITHMS FOR PORTFOLIO ALLOCATION By Andrew Bolin A Thesis. INTRODUCTION AND HISTORICAL REVIEW . . . . . . . . . . . . . . 1 1.1 Modern Portfolio Theory . . . . . . 6 2.1.5 Minimizing MDD Subject to a Return Constraint (Min-MDD) 6 2.2 Clustering
Genetic algorithm optimization of entanglement
Navarro-Mun'oz, Jorge C.; Rosu, H. C.; Lopez-Sandoval, R. [Potosinian Institute of Science and Technology, Apartado Postal 3-74 Tangamanga, 78231 San Luis Potosi (Mexico)
We present an application of the genetic algorithmic computational method to the optimization of the concurrence measure of entanglement for the cases of one dimensional chains, as well as square and triangular lattices in a simple tight-binding approach in which the hopping of electrons is much stronger than the phonon dissipation.
Genetic algorithm optimization of entanglement
Jorge C. Navarro-Munoz; H. C. Rosu; R. Lopez-Sandoval
We present an application of a genetic algorithmic computational method to the optimization of the concurrence measure of entanglement for the cases of one dimensional chains, as well as square and triangular lattices in a simple tight-binding approach in which the hopping of electrons is much stronger than the phonon dissipation
Multilevel algorithms for nonlinear optimization
NASA Technical Reports Server (NTRS)
Alexandrov, Natalia; Dennis, J. E., Jr.
Multidisciplinary design optimization (MDO) gives rise to nonlinear optimization problems characterized by a large number of constraints that naturally occur in blocks. We propose a class of multilevel optimization methods motivated by the structure and number of constraints and by the expense of the derivative computations for MDO. The algorithms are an extension to the nonlinear programming problem of the successful class of local Brown-Brent algorithms for nonlinear equations. Our extensions allow the user to partition constraints into arbitrary blocks to fit the application, and they separately process each block and the objective function, restricted to certain subspaces. The methods use trust regions as a globalization strategy, and they have been shown to be globally convergent under reasonable assumptions. The multilevel algorithms can be applied to all classes of MDO formulations. Multilevel algorithms for solving nonlinear systems of equations are a special case of the multilevel optimization methods. In this case, they can be viewed as a trust-region globalization of the Brown-Brent class.
Optimal seismic deconvolution: distributed algorithms
Konstantinos N. Plataniotis; Sokratis K. Katsikas; Demetrios G. Lainiotis; Anastasios N. Venetsanopoulos
Deconvolution is one of the most important aspects of seismic signal processing. The objective of the deconvolution procedure is to remove the obscuring effect of the wavelet's replica making up the seismic trace and therefore obtain an estimate of the reflection coefficient sequence. This paper introduces a new deconvolution algorithm. Optimal distributed estimators and smoothers are utilized in the proposed
A Hybrid PSO/ACO Algorithm for Classification Nicholas Holden
Heinke, Dietmar
A Hybrid PSO/ACO Algorithm for Classification Nicholas Holden University of Kent Computing In a previous work we have proposed a hybrid Particle Swarm Optimisation/Ant Colony Optimisation (PSO PSO algorithm, this hybrid algorithm can directly cope with nominal attributes, without converting
Firefly Algorithm, L�vy Flights and Global Optimization
NASA Astrophysics Data System (ADS)
Yang, Xin-She
Nature-inspired algorithms such as Particle Swarm Optimization and Firefly Algorithm are among the most powerful algorithms for optimization. In this paper, we intend to formulate a new metaheuristic algorithm by combining L�vy flights with the search strategy via the Firefly Algorithm. Numerical studies and results suggest that the proposed L�vy-flight firefly algorithm is superior to existing metaheuristic algorithms. Finally implications for further research and wider applications will be discussed.
Firefly Algorithm, Levy Flights and Global Optimization
Yang, Xin-She
Nature-inspired algorithms such as Particle Swarm Optimization and Firefly Algorithm are among the most powerful algorithms for optimization. In this paper, we intend to formulate a new metaheuristic algorithm by combining Levy flights with the search strategy via the Firefly Algorithm. Numerical studies and results suggest that the proposed Levy-flight firefly algorithm is superior to existing metaheuristic algorithms. Finally implications for further research and wider applications will be discussed.
MODELING, VERIFICATION AND OPTIMIZATION OF HYBRID GROUND SOURCE HEAT PUMP SYSTEMS IN ENERGYPLUS, VERIFICATION AND OPTIMIZATION OF HYBRID GROUND SOURCE HEAT PUMP SYSTEMS IN ENERGYPLUS Thesis Approved by: Dr.................................................................................................................... 16 MODELING OF HYBRID GROUND SOURCE HEAT PUMP SYSTEMS IN ENERGYPLUS
On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems
M. Senthil Arumugam; M. V. C. Rao
This paper deals with the concept of including the popular genetic algorithm operator, cross-over and root mean square (RMS) variants into particle swarm optimization (PSO) algorithm to make the convergence faster. Two different PSO algorithms are considered in this paper: the first one is the conventional PSO (cPSO) and the second is the global-local best values based PSO (GLbest-PSO). The
A hybrid of the genetic algorithm and concurrent simplex
Randolph, David Ethan
1995 Major Subject: Computer Science A HYBRID OF THE GENETIC ALGORITHM AND CONCURRENT SIMPLEX A Thesis DAVID ETHAN RANDOLPH Submitted to Texas AkM University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE... THE GENETIC ALGORITHM A. The Innards of the Genetic Algorithm. . . 1. A Toy Problem 2. The Works 3. A Second Toy Problem B. The Effectiveness of the Genetic Algorithm . C. Previous Genetic Algorithm Hybrids 1. Pipelining Hybrids . 2. Abstraction...
Optimizing the specificity of nucleic acid hybridization
Zhang, David Yu
Optimizing the specificity of nucleic acid hybridization David Yu Zhang1,2 *, Sherry Xi Chen3, the specificity of nucleic acid hybridization is compromised for long strands, except near the melting temperature to 37 88888C, from 1 mM Mg21 to 47 mM Mg21 , and with nucleic acid concentrations from 1 nM to 5 m
Time series prediction with recurrent neural networks trained by a hybrid PSO-EA algorithm
Xindi Cai; Nian Zhang; Ganesh K. Venayagamoorthy; Donald C. Wunsch II
Abstract To predict the 100 missing values from a time series of 5000 data points, given for the IJCNN 2004 time series prediction competition, recurrent neural networks,(RNNs) are trained with a new,learning algorithm. This training algorithm,is based on a hybrid of particle swarm,optimization,(PSO) and,evolutionary,algorithm,(EA). By combining,the searching,abilities of these two,global optimization methods, the evolution of individuals is no longer restricted
A Hybrid Rough Set--Particle Swarm Algorithm for Image Pixel Classification
Swagatam Das; Ajith Abraham; Subir Kumar Sarkar
This article presents a framework to hybridize the rough set theory with a famous swarm intelligence algorithm known as Particle Swarm Optimization (PSO). The hybrid rough-PSO technique has been used for grouping the pixels of an image in its intensity space. Medical and remote sensing satellite images become corrupted with noise very often. Fast and efficient segmentation of such noisy
A New Optimized GA-RBF Neural Network Algorithm
Zhao, Dean; Su, Chunyang; Hu, Chanli; Zhao, Yuyan
When confronting the complex problems, radial basis function (RBF) neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and recognition precision. Aiming at this problem, we propose a new optimized RBF neural network algorithm based on genetic algorithm (GA-RBF algorithm), which uses genetic algorithm to optimize the weights and structure of RBF neural network; it chooses new ways of hybrid encoding and optimizing simultaneously. Using the binary encoding encodes the number of the hidden layer's neurons and using real encoding encodes the connection weights. Hidden layer neurons number and connection weights are optimized simultaneously in the new algorithm. However, the connection weights optimization is not complete; we need to use least mean square (LMS) algorithm for further leaning, and finally get a new algorithm model. Using two UCI standard data sets to test the new algorithm, the results show that the new algorithm improves the operating efficiency in dealing with complex problems and also improves the recognition precision, which proves that the new algorithm is valid. PMID:25371666
A new optimized GA-RBF neural network algorithm.
Jia, Weikuan; Zhao, Dean; Shen, Tian; Su, Chunyang; Hu, Chanli; Zhao, Yuyan
When confronting the complex problems, radial basis function (RBF) neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and recognition precision. Aiming at this problem, we propose a new optimized RBF neural network algorithm based on genetic algorithm (GA-RBF algorithm), which uses genetic algorithm to optimize the weights and structure of RBF neural network; it chooses new ways of hybrid encoding and optimizing simultaneously. Using the binary encoding encodes the number of the hidden layer's neurons and using real encoding encodes the connection weights. Hidden layer neurons number and connection weights are optimized simultaneously in the new algorithm. However, the connection weights optimization is not complete; we need to use least mean square (LMS) algorithm for further leaning, and finally get a new algorithm model. Using two UCI standard data sets to test the new algorithm, the results show that the new algorithm improves the operating efficiency in dealing with complex problems and also improves the recognition precision, which proves that the new algorithm is valid. PMID:25371666
Coupled Low-thrust Trajectory and System Optimization via Multi-Objective Hybrid Optimal Control
NASA Technical Reports Server (NTRS)
Vavrina, Matthew A.; Englander, Jacob Aldo; Ghosh, Alexander R.
The optimization of low-thrust trajectories is tightly coupled with the spacecraft hardware. Trading trajectory characteristics with system parameters ton identify viable solutions and determine mission sensitivities across discrete hardware configurations is labor intensive. Local independent optimization runs can sample the design space, but a global exploration that resolves the relationships between the system variables across multiple objectives enables a full mapping of the optimal solution space. A multi-objective, hybrid optimal control algorithm is formulated using a multi-objective genetic algorithm as an outer loop systems optimizer around a global trajectory optimizer. The coupled problem is solved simultaneously to generate Pareto-optimal solutions in a single execution. The automated approach is demonstrated on two boulder return missions.
Impact of battery sizing on stochastic optimal power management in plug-in hybrid electric vehicles
Scott J. Moura; Duncan S. Callaway; Hosam K. Fathy; Jeffrey L. Stein
This paper examines the impact of battery sizing on the performance and efficiency of power management algorithms in plug-in hybrid electric vehicles (PHEVs). Existing studies examine this impact for power management algorithms derived using either rule-based or deterministic dynamic programming methods. This paper extends the above investigations to power management algorithms optimized using stochastic dynamic programming (SDP). The paper treats
Enhanced hybrid search algorithm for protein structure prediction using the 3D-HP lattice model.
Zhou, Changjun; Hou, Caixia; Zhang, Qiang; Wei, Xiaopeng
The problem of protein structure prediction in the hydrophobic-polar (HP) lattice model is the prediction of protein tertiary structure. This problem is usually referred to as the protein folding problem. This paper presents a method for the application of an enhanced hybrid search algorithm to the problem of protein folding prediction, using the three dimensional (3D) HP lattice model. The enhanced hybrid search algorithm is a combination of the particle swarm optimizer (PSO) and tabu search (TS) algorithms. Since the PSO algorithm entraps local minimum in later evolution extremely easily, we combined PSO with the TS algorithm, which has properties of global optimization. Since the technologies of crossover and mutation are applied many times to PSO and TS algorithms, so enhanced hybrid search algorithm is called the MCMPSO-TS (multiple crossover and mutation PSO-TS) algorithm. Experimental results show that the MCMPSO-TS algorithm can find the best solutions so far for the listed benchmarks, which will help comparison with any future paper approach. Moreover, real protein sequences and Fibonacci sequences are verified in the 3D HP lattice model for the first time. Compared with the previous evolutionary algorithms, the new hybrid search algorithm is novel, and can be used effectively to predict 3D protein folding structure. With continuous development and changes in amino acids sequences, the new algorithm will also make a contribution to the study of new protein sequences. PMID:23824509
A hybrid neural approach to combinatorial optimization
Kate A. Smith; Marimuthu Palaniswami; M. Krishnamoorthy
Both the Hopfield neural network and Kohonen's principles of self-organization have been used to solve difficult optimization problems, with varying degrees of success. In this paper, a hybrid neural network is presented which combines, for the first time, a new self-organizing approach to optimization with a Hopfield network. It is demonstrated that many of the traditional problems associated with each
Genetic Algorithm for Optimization: Preprocessor and Algorithm
NASA Technical Reports Server (NTRS)
Sen, S. K.; Shaykhian, Gholam A.
Genetic algorithm (GA) inspired by Darwin's theory of evolution and employed to solve optimization problems - unconstrained or constrained - uses an evolutionary process. A GA has several parameters such the population size, search space, crossover and mutation probabilities, and fitness criterion. These parameters are not universally known/determined a priori for all problems. Depending on the problem at hand, these parameters need to be decided such that the resulting GA performs the best. We present here a preprocessor that achieves just that, i.e., it determines, for a specified problem, the foregoing parameters so that the consequent GA is a best for the problem. We stress also the need for such a preprocessor both for quality (error) and for cost (complexity) to produce the solution. The preprocessor includes, as its first step, making use of all the information such as that of nature/character of the function/system, search space, physical/laboratory experimentation (if already done/available), and the physical environment. It also includes the information that can be generated through any means - deterministic/nondeterministic/graphics. Instead of attempting a solution of the problem straightway through a GA without having/using the information/knowledge of the character of the system, we would do consciously a much better job of producing a solution by using the information generated/created in the very first step of the preprocessor. We, therefore, unstintingly advocate the use of a preprocessor to solve a real-world optimization problem including NP-complete ones before using the statistically most appropriate GA. We also include such a GA for unconstrained function optimization problems.
Trimming Aircraft on the Ground Based on the Hybrid Genetic Algorithm
Zhang Lei; Huang Qitao; Jiang Hongzhou; Han Junwei
Trimming aircraft on the ground is an absolutely necessary for flight simulators. In order to carry out this task, the aircraft-runway dynamic model is analyzed, and then an elite re-optimized hybrid genetic algorithm for trimming the model is designed, which combines the merits of a genetic algorithm and pattern search. It does not only avoid being trapped by a local
Coello, Carlos A. Coello
IMPLEMENTATION OF LOCAL SEARCH IN HYBRID MULTI-OBJECTIVE GENETIC ALGORITHMS: A CASE STUDY the following issues related to the implementation of local search in hybrid multi-objective genetic algorithms: specification of an objective function to be optimized by local search, early termination of local search before
Evolutionary optimization algorithm by entropic sampling
NASA Astrophysics Data System (ADS)
Lee, Chang-Yong; Han, Seung Kee
A combinatorial optimization algorithm, genetic-entropic algorithm, is proposed. This optimization algorithm is based on the genetic algorithms and the natural selection via entropic sampling. With the entropic sampling, this algorithm helps to escape local optima in the complex optimization problems. To test the performance of the algorithm, we adopt the NK model (N is the number of bits in the string and K is the degree of epistasis) and compare the performances of the proposed algorithm with those of the canonical genetic algorithm. It is found that the higher the K value, the better this algorithm can escape local optima and search near global optimum. The characteristics of this algorithm in terms of the power spectrum analysis together with the difference between two algorithms are discussed.
Parameter estimation for chaotic systems using a hybrid adaptive cuckoo search with simulated annealing algorithm
Sheng, Zheng, E-mail: email@example.com [College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101 (China)] [College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101 (China); Wang, Jun; Zhou, Bihua [National Defense Key Laboratory on Lightning Protection and Electromagnetic Camouflage, PLA University of Science and Technology, Nanjing 210007 (China)] [National Defense Key Laboratory on Lightning Protection and Electromagnetic Camouflage, PLA University of Science and Technology, Nanjing 210007 (China); Zhou, Shudao [College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101 (China) [College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101 (China); Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044 (China)
This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.
Optimizing hybrid spreading in metapopulations.
Zhang, Changwang; Zhou, Shi; Miller, Joel C; Cox, Ingemar J; Chain, Benjamin M
Epidemic spreading phenomena are ubiquitous in nature and society. Examples include the spreading of diseases, information, and computer viruses. Epidemics can spread by local spreading, where infected nodes can only infect a limited set of direct target nodes and global spreading, where an infected node can infect every other node. In reality, many epidemics spread using a hybrid mixture of both types of spreading. In this study we develop a theoretical framework for studying hybrid epidemics, and examine the optimum balance between spreading mechanisms in terms of achieving the maximum outbreak size. We show the existence of critically hybrid epidemics where neither spreading mechanism alone can cause a noticeable spread but a combination of the two spreading mechanisms would produce an enormous outbreak. Our results provide new strategies for maximising beneficial epidemics and estimating the worst outcome of damaging hybrid epidemics. PMID:25923411
Comparative Study of Derivative Free Optimization Algorithms
Nam Pham; A. Malinowski; T. Bartczak
Derivative free optimization algorithms are often used when it is difficult to find function derivatives, or if finding such derivatives are time consuming. The Nelder Mead's simplex method is one of the most popular derivative free optimization algorithms in the fields of engineering, statistics, and sciences. This algorithm is favored and widely used because of its fast con- vergence and
Zs. Preitl; P. Bauer; J. Bokor
Abstract: Hybrid electric vehicles (HEVs), having multiple main energy sources, are an attractive alternative to conventional,vehicles. The paper presents a study,on minimizing the energy consumption,in a series hybrid solar vehicle (HSV). First a description,of the series HSV is given, after which two control strategies are presented for fuel consumption optimization. The first control strategy is dynamic ,programming ,(DP) which ,is
Design of Two-Dimensional Infinite Impulse Response Recursive Filters Using Hybrid Multiagent Particle Swarm Optimization
Rajesh Kumar; Anupam Kumar
We incorporate the optimization problem of two-dimensional infinite impulse response (IIR) recursive filters and the optimization methodology of hybrid multiagent particle swarm optimization (HMAPSO) and then apply the resultant optimized IIR filter in image processing for justifying HMAPSO robustness over other algorithm and its role in optimizing real-time situations. The design of the 2-D IIR filter is reduced to a
Global optimization algorithms for a CAD workstation
W. L. Price
This paper describes two new versions of the controlled random search procedure for global optimization (CRS). Designed primarily to suit the user of a CAD workstation, these algorithms can also be used effectively in other contexts. The first, known as CRS3, speeds the final convergence of the optimization by combining a local optimization algorithm with the global search procedure. The
Hybrid algorithms in quantum Monte Carlo
Esler, Kenneth P [ORNL] [ORNL; Mcminis, Jeremy [University of Illinois, Urbana-Champaign] [University of Illinois, Urbana-Champaign; Morales, Miguel A [Lawrence Livermore National Laboratory (LLNL)] [Lawrence Livermore National Laboratory (LLNL); Clark, Bryan K. [Princeton University] [Princeton University; Shulenburger, Luke [Sandia National Laboratory (SNL)] [Sandia National Laboratory (SNL); Ceperley, David M [ORNL] [ORNL
With advances in algorithms and growing computing powers, quantum Monte Carlo (QMC) methods have become a leading contender for high accuracy calculations for the electronic structure of realistic systems. The performance gain on recent HPC systems is largely driven by increasing parallelism: the number of compute cores of a SMP and the number of SMPs have been going up, as the Top500 list attests. However, the available memory as well as the communication and memory bandwidth per element has not kept pace with the increasing parallelism. This severely limits the applicability of QMC and the problem size it can handle. OpenMP/MPI hybrid programming provides applications with simple but effective solutions to overcome efficiency and scalability bottlenecks on large-scale clusters based on multi/many-core SMPs. We discuss the design and implementation of hybrid methods in QMCPACK and analyze its performance on current HPC platforms characterized by various memory and communication hierarchies.
A novel bee swarm optimization algorithm for numerical function optimization
Reza Akbari; Alireza Mohammadi; Koorush Ziarati
The optimization algorithms which are inspired from intelligent behavior of honey bees are among the most recently introduced population based techniques. In this paper, a novel algorithm called bee swarm optimization, or BSO, and its two extensions for improving its performance are presented. The BSO is a population based optimization technique which is inspired from foraging behavior of honey bees.
Hybrid Harmony Search Algorithm and Interior Point Method for Economic Dispatch with Valve-Point Effect
NASA Astrophysics Data System (ADS)
Sivasubramani, S.; Ahmad, Md. Samar
This paper proposes a new hybrid algorithm combining harmony search (HS) algorithm and interior point method (IPM) for economic dispatch (ED) problem with valve-point effect. ED problem with valve-point effect is modeled as a non-linear, constrained and non-convex optimization problem having several local minima. IPM is a best non-linear optimization method for convex optimization problems. Since ED problem with valve-point effect has multiple local minima, IPM results in a local optimum solution. In order to avoid IPM getting trapped in a local optimum, a new evolutionary algorithm HS, which is good in global exploration, has been combined. In the hybrid method, HS is used for global search and IPM for local search. The hybrid method has been tested on three different test systems to prove its effectiveness. Finally, the simulation results are also compared with other methods reported in the literature.
Assessing the Performance of Two Immune Inspired Algorithms and a Hybrid Genetic Algorithm for
Kent, University of
Assessing the Performance of Two Immune Inspired Algorithms and a Hybrid Genetic Algorithm.ac.uk Abstract- Do Artificial Immune Systems (AIS) have something to offer the world of optimisation? Indeed do immune inspired algorithms and a hybrid genetic algorithm for function optimisation. This work is part
Experimental Comparisons of Derivative Free Optimization Algorithms
Auger, Anne; Zerpa, Jorge M Perez; Ros, Raymond; Schoenauer, Marc
In this paper, the performances of the quasi-Newton BFGS algorithm, the NEWUOA derivative free optimizer, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), the Differential Evolution (DE) algorithm and Particle Swarm Optimizers (PSO) are compared experimentally on benchmark functions reflecting important challenges encountered in real-world optimization problems. Dependence of the performances in the conditioning of the problem and rotational invariance of the algorithms are in particular investigated.
An improved GA and a novel PSO-GA-based hybrid algorithm
X. H. Shi; Y. C. Liang; H. P. Lee; C. Lu; L. M. Wang
Inspired by the natural features of the variable size of the population, we present a variable population-size genetic algorithm (VPGA) by introducing the �dying probability� for the individuals and the �war\\/disease process� for the population. Based on the VPGA and the particle swarm optimization (PSO) algorithms, a novel PSO-GA-based hybrid algorithm (PGHA) is also proposed in this paper. Simulation results
Optimum design of short journal bearings by enhanced artificial life optimization algorithm
Jin-Dae Song; Bo-Suk Yang; Byeong-Gun Choi; Hyung-Ja Kim
This paper presents an optimum design of high-speed short journal bearing using an enhanced artificial life algorithm (EALA) to compute the solutions of optimization problem. The proposed hybrid EALA algorithm is a synthesis of an artificial life algorithm (ALA) and the random tabu search method (R-tabu method) to solve some demerits of the ALA. The emergence is the most important
Optimal Scheduling of Cascade Hydropower System Using Grouping Differential Evolution Algorithm
Yinghai Li; Jian Zuo
For the complex problem of cascade hydropower system optimal scheduling, a novel grouping differential evolution algorithm (GDE) is proposed in this paper by hybridizing differential evolution (DE) and shuffled frog leaping (SFL). In the proposed algorithm, the population is periodically executes grouping and shuffling operations, and the individuals are updated according to differential evolution in each memplex. Finally, the algorithm
Optimal control of parallel hybrid electric vehicles
Antonio Sciarretta; Michael Back; Lino Guzzella
In this paper, a model-based strategy for the real-time load control of parallel hybrid vehicles is presented. The aim is to develop a fuel-optimal control which is not relying on the a priori knowledge of the future driving conditions (global optimal control), but only upon the current system operation. The methodology developed is valid for those problem that are characterized
Generation of Compliant Mechanisms using Hybrid Genetic Algorithm
NASA Astrophysics Data System (ADS)
Sharma, D.; Deb, K.
Compliant mechanism is a single piece elastic structure which can deform to perform the assigned task. In this work, compliant mechanisms are evolved using a constraint based bi-objective optimization formulation which requires one user defined parameter ( ?). This user defined parameter limits a gap between a desired path and an actual path traced by the compliant mechanism. The non-linear and discrete optimization problems are solved using the hybrid Genetic Algorithm (GA) wherein domain specific initialization, two-dimensional crossover operator and repairing techniques are adopted. A bit-wise local search method is used with elitist non-dominated sorting genetic algorithm to further refine the compliant mechanisms. Parallel computations are performed on the master-slave architecture to reduce the computation time. A parametric study is carried out for ? value which suggests a range to evolve topologically different compliant mechanisms. The applied and boundary conditions to the compliant mechanisms are considered the variables that are evolved by the hybrid GA. The post-analysis of results unveils that the complaint mechanisms are always supported at unique location that can evolve the non-dominated solutions.
Simulated annealing algorithm for optimal capital growth
NASA Astrophysics Data System (ADS)
Luo, Yong; Zhu, Bo; Tang, Yong
We investigate the problem of dynamic optimal capital growth of a portfolio. A general framework that one strives to maximize the expected logarithm utility of long term growth rate was developed. Exact optimization algorithms run into difficulties in this framework and this motivates the investigation of applying simulated annealing optimized algorithm to optimize the capital growth of a given portfolio. Empirical results with real financial data indicate that the approach is inspiring for capital growth portfolio.
Regional planning strategies for hybrid energy optimization
A. Bowen
A review is presented of available fuels and energy sources with regional planning strategies for hybrid energy optimization. The fuels include coal, crops, nuclear sources, geothermal, hydraulic power, and solar; energy sources are classified as genotypes which are easily applied at scaled levels within a defined region, and phenotypes which are modifications of regional sources to suit any site within
Structural inverse analysis by hybrid simplex artificial bee colony algorithms
Fei Kang; Junjie Li; Qing Xu
A hybrid simplex artificial bee colony algorithm (HSABCA) which combines Nelder�Mead simplex method with artificial bee colony algorithm (ABCA) is proposed for inverse analysis problems. The proposed algorithm is applied to parameter identification of concrete dam-foundation systems. To verify the performance of HSABCA, it is compared with the basic ABCA and a real coded genetic algorithm (RCGA) on two examples:
Research on traction slip control algorithm for parallel hybrid cars
Liang Chu; Li Bo Chao; Zhan Wu; Tong Bo Wu
Traction slip control algorithm and strategy for parallel hybrid vehicle are proposed in this paper. Based on the torque distribution strategy of parallel cars, the torque control strategy and algorithm and brake control strategy of TCS are designed. Under the environment of Matlab\\/Simulink, the vehicle model and TCS controller model of parallel Hybrid Electric vehicle are built. The simulation test
A. Sartenaer
This article provides a condensed overview of some of the major today's features (both classical or recently developed), used in the design and development of algorithms to solve nonlinear continuous optimization problems. We rst consider the unconstrained optimization case to introduce the line-search and trust-region approaches as globalization techniques to force an algorithm to converge from any starting point. We
Optimal RF design using smart evolutionary algorithms
Peter J. Vancorenland; Carl De Ranter; Michiel Steyaert; Georges G. E. Gielen
This paper presents an optimization algorithm that is able to significantly increase the speed of RF circuit optimizations. The algorithm consists of a series of consecutive evolutionary opti- mizations of the circuit itself and of a modeled version thereof. The speed increase arises from the difference in evaluation time between the real simulation and the fit evaluation. As circuit ap-
Stochastic Search for Signal Processing Algorithm Optimization
Stochastic Search for Signal Processing Algorithm Optimization Bryan Singer Manuela Veloso May address the complex task of signal processing optimization. We first introduce and discuss the complexities of this domain. In general, a single signal processing algorithm can be represented by a very
An improved particle swarm optimization algorithm
Yan Jiang; Tiesong Hu; Chongchao Huang; Xianing Wu
An improved particle swarm optimization (IPSO) is proposed in this paper. In the new algorithm, a population of points sampled randomly from the feasible space. Then the population is partitioned into several sub-swarms, each of which is made to evolve based on particle swarm optimization (PSO) algorithm. At periodic stages in the evolution, the entire population is shuffled, and then
A modification to particle swarm optimization algorithm
Huiyuan Fan
In this paper, a modification strategy is proposed for the particle swarm optimization (PSO) algorithm. The strategy adds an adaptive scaling term into the algorithm, which aims to increase its convergence rate and thereby to obtain an acceptable solution with a lower number of objective function evaluations. Such an improvement can be useful in many practical engineering optimizations where the
Hybrid metrology universal engine: co-optimization
NASA Astrophysics Data System (ADS)
Vaid, Alok; Osorio, Carmen; Tsai, Jamie; Bozdog, Cornel; Sendelbach, Matthew; Grubner, Eyal; Koret, Roy; Wolfling, Shay
In recent years Hybrid Metrology has emerged as an option for enhancing the performance of existing measurement toolsets and is currently implemented in production1. Hybrid Metrology is the practice to combine measurements from multiple toolset types in order to enable or improve the measurement of one or more critical parameters. While all applications tried before were improved through standard (sequential) hybridization of data from one toolset to another, advances in device architecture, materials and processes made possible to find one case that demanded a much deeper understanding of the physical basis of measurements and simultaneous optimization of data. This paper presents the first such work using the concept of co-optimization based hybridization, where image analysis parameters of CD-SEM (critical dimensions Scanning Electron Microscope) are modulated by profile information from OCD (optical critical dimension - scatterometry) while the OCD extracted profile is concurrently optimized through addition of the CD-SEM CD results. Test vehicle utilized in this work is the 14nm technology node based FinFET High-k/Interfacial layer structure.
Traffic sharing algorithms for hybrid mobile networks
NASA Technical Reports Server (NTRS)
Arcand, S.; Murthy, K. M. S.; Hafez, R.
In a hybrid (terrestrial + satellite) mobile personal communications networks environment, a large size satellite footprint (supercell) overlays on a large number of smaller size, contiguous terrestrial cells. We assume that the users have either a terrestrial only single mode terminal (SMT) or a terrestrial/satellite dual mode terminal (DMT) and the ratio of DMT to the total terminals is defined gamma. It is assumed that the call assignments to and handovers between terrestrial cells and satellite supercells take place in a dynamic fashion when necessary. The objectives of this paper are twofold, (1) to propose and define a class of traffic sharing algorithms to manage terrestrial and satellite network resources efficiently by handling call handovers dynamically, and (2) to analyze and evaluate the algorithms by maximizing the traffic load handling capability (defined in erl/cell) over a wide range of terminal ratios (gamma) given an acceptable range of blocking probabilities. Two of the algorithms (G & S) in the proposed class perform extremely well for a wide range of gamma.
A Multiobjective Optimization Framework for Online Stochastic Optimal Control in Hybrid Electric Vehicles
Malikopoulos, Andreas [ORNL
The increasing urgency to extract additional efficiency from hybrid propulsion systems has led to the development of advanced power management control algorithms. In this paper we address the problem of online optimization of the supervisory power management control in parallel hybrid electric vehicles (HEVs). We model HEV operation as a controlled Markov chain and we show that the control policy yielding the Pareto optimal solution minimizes online the long-run expected average cost per unit time criterion. The effectiveness of the proposed solution is validated through simulation and compared to the solution derived with dynamic programming using the average cost criterion. Both solutions achieved the same cumulative fuel consumption demonstrating that the online Pareto control policy is an optimal control policy.
Design of optimal correlation filters for hybrid vision systems
NASA Technical Reports Server (NTRS)
Rajan, Periasamy K.
Research is underway at the NASA Johnson Space Center on the development of vision systems that recognize objects and estimate their position by processing their images. This is a crucial task in many space applications such as autonomous landing on Mars sites, satellite inspection and repair, and docking of space shuttle and space station. Currently available algorithms and hardware are too slow to be suitable for these tasks. Electronic digital hardware exhibits superior performance in computing and control; however, they take too much time to carry out important signal processing operations such as Fourier transformation of image data and calculation of correlation between two images. Fortunately, because of the inherent parallelism, optical devices can carry out these operations very fast, although they are not quite suitable for computation and control type operations. Hence, investigations are currently being conducted on the development of hybrid vision systems that utilize both optical techniques and digital processing jointly to carry out the object recognition tasks in real time. Algorithms for the design of optimal filters for use in hybrid vision systems were developed. Specifically, an algorithm was developed for the design of real-valued frequency plane correlation filters. Furthermore, research was also conducted on designing correlation filters optimal in the sense of providing maximum signal-to-nose ratio when noise is present in the detectors in the correlation plane. Algorithms were developed for the design of different types of optimal filters: complex filters, real-value filters, phase-only filters, ternary-valued filters, coupled filters. This report presents some of these algorithms in detail along with their derivations.
Design of optimal correlation filters for hybrid vision systems
NASA Astrophysics Data System (ADS)
Rajan, Periasamy K.
Research is underway at the NASA Johnson Space Center on the development of vision systems that recognize objects and estimate their position by processing their images. This is a crucial task in many space applications such as autonomous landing on Mars sites, satellite inspection and repair, and docking of space shuttle and space station. Currently available algorithms and hardware are too slow to be suitable for these tasks. Electronic digital hardware exhibits superior performance in computing and control; however, they take too much time to carry out important signal processing operations such as Fourier transformation of image data and calculation of correlation between two images. Fortunately, because of the inherent parallelism, optical devices can carry out these operations very fast, although they are not quite suitable for computation and control type operations. Hence, investigations are currently being conducted on the development of hybrid vision systems that utilize both optical techniques and digital processing jointly to carry out the object recognition tasks in real time. Algorithms for the design of optimal filters for use in hybrid vision systems were developed. Specifically, an algorithm was developed for the design of real-valued frequency plane correlation filters. Furthermore, research was also conducted on designing correlation filters optimal in the sense of providing maximum signal-to-nose ratio when noise is present in the detectors in the correlation plane. Algorithms were developed for the design of different types of optimal filters: complex filters, real-value filters, phase-only filters, ternary-valued filters, coupled filters. This report presents some of these algorithms in detail along with their derivations.
Hybridizing Evolutionary Algorithms and Clustering Algorithms to Find Source-Code Clones
Maletic, Jonathan I.
Hybridizing Evolutionary Algorithms and Clustering Algorithms to Find Source-Code Clones Andrew a hybrid approach to detect source-code clones that combines evolutionary algorithms and clustering. A case is effective in detecting groups of source-code clones. Categories and Subject Descriptors D.2.7 [Software
An adaptive simple particle swarm optimization algorithm
Fan Chunxia; Wan Youhong
The particle swarm optimization algorithm with constriction factor (CFPSO) has some demerits, such as relapsing into local extremum, slow convergence velocity and low convergence precision in the late evolutionary. An adaptive simple particle swarm optimization with constriction factor (AsCFPSO) is combined with chaotic optimization, then a new CFPSO is developed, i.e., a chaotic optimization-based adaptive simple particle swarm optimization equation
A modified particle swarm optimization algorithm
Qian-Li Zhang; Xing Li; Quang-Ahn Tran
A modified particle swarm optimization (PSO) algorithm is proposed in this paper to avoid premature convergence with the introduction of mutation operation. The performance of this algorithm is compared to the standard PSO algorithm and experiments indicate that it has better performance with little overhead.
Intelligent perturbation algorithms to space scheduling optimization
NASA Technical Reports Server (NTRS)
Kurtzman, Clifford R.
The limited availability and high cost of crew time and scarce resources make optimization of space operations critical. Advances in computer technology coupled with new iterative search techniques permit the near optimization of complex scheduling problems that were previously considered computationally intractable. Described here is a class of search techniques called Intelligent Perturbation Algorithms. Several scheduling systems which use these algorithms to optimize the scheduling of space crew, payload, and resource operations are also discussed.
A New Multi-objective Particle Swarm Optimization Algorithm Using Clustering Applied to Automated Docking
Stefan Janson; Daniel Merkle
\\u000a In this paper we introduce the new hybrid Particle Swarm Optimization algorithm for multi-objective optimization ClustMPSO.\\u000a We combined the PSO algorithm with clustering techniques to divide all particles into several subswarms. Strategies for updating\\u000a the personal best position of a particle, for selection of the neighbourhood best and for swarm dominance are proposed. The\\u000a algorithm is analyzed on both artificial
Comparison of optimization algorithms for piecewise linear discriminant analysis: application to Fourier transform infrared remote sensing measurements
Ronald E. Shaffer; Gary W. Small
Simplex optimization, simulated annealing, generalized simulated annealing, genetic algorithms, and a Simplex-genetic algorithm hybrid are compared for their ability to optimize piecewise linear discriminants. Nonparametric piecewise linear discriminant analysis (PLDA) is employed here to develop an automated detection scheme for Fourier transform infrared remote sensing interferogram data. Piecewise linear discriminants are computed and optimized for interferograms collected when sulfur hexafluoride,
The Tunneling Hybrid Monte-Carlo algorithm
Maarten Golterman; Yigal Shamir
The hermitian Wilson kernel used in the construction of the domain-wall and overlap Dirac operators has exceptionally small eigenvalues that make it expensive to reach high-quality chiral symmetry for domain-wall fermions, or high precision in the case of the overlap operator. An efficient way of suppressing such eigenmodes consists of including a positive power of the determinant of the Wilson kernel in the Boltzmann weight, but doing this also suppresses tunneling between topological sectors. Here we propose a modification of the Hybrid Monte-Carlo algorithm which aims to restore tunneling between topological sectors by excluding the lowest eigenmodes of the Wilson kernel from the molecular-dynamics evolution, and correcting for this at the accept/reject step. We discuss the implications of this modification for the acceptance rate.
An Algorithm for Optimal PLA Folding
Gary D. Hachtel; A. Richard Newton; Alberto L. Sangiovanni-vincentelli
In this paper we present a graph-theoretic formulation of the optimal PLA folding problem. The class of admissible PLA foldings is defined. Necessary and sufficient conditions for obtaining the optimal folding are given. A subproblem of the optimal problem is shown to be NP-complete, and a heuristic algorithm is given which has proven to be effective on a number of
A fully adaptive hybrid optimization of aircraft engine blades
NASA Astrophysics Data System (ADS)
Dumas, L.; Druez, B.; Lecerf, N.
A new fully adaptive hybrid optimization method (AHM) has been developed and applied to an industrial problem in the field of the aircraft engine industry. The adaptivity of the coupling between a global search by a population-based method (Genetic Algorithms or Evolution Strategies) and the local search by a descent method has been particularly emphasized. On various analytical test cases, the AHM method overperforms the original global search method in terms of computational time and accuracy. The results obtained on the industrial case have also confirmed the interest of AHM for the design of new and original solutions in an affordable time.
Full Glowworm Swarm Optimization Algorithm for Whole-Set Orders Scheduling in Single Machine
Yu, Zhang; Yang, Xiaomei
By analyzing the characteristics of whole-set orders problem and combining the theory of glowworm swarm optimization, a new glowworm swarm optimization algorithm for scheduling is proposed. A new hybrid-encoding schema combining with two-dimensional encoding and random-key encoding is given. In order to enhance the capability of optimal searching and speed up the convergence rate, the dynamical changed step strategy is integrated into this algorithm. Furthermore, experimental results prove its feasibility and efficiency. PMID:24294135
Restarted local search algorithms for continuous black box optimization.
Po��k, Petr; Huyer, Waltraud
Several local search algorithms for real-valued domains (axis parallel line search, Nelder-Mead simplex search, Rosenbrock's algorithm, quasi-Newton method, NEWUOA, and VXQR) are described and thoroughly compared in this article, embedding them in a multi-start method. Their comparison aims (1) to help the researchers from the evolutionary community to choose the right opponent for their algorithm (to choose an opponent that would constitute a hard-to-beat baseline algorithm), (2) to describe individual features of these algorithms and show how they influence the algorithm on different problems, and (3) to provide inspiration for the hybridization of evolutionary algorithms with these local optimizers. The recently proposed Comparing Continuous Optimizers (COCO) methodology was adopted as the basis for the comparison. The results show that in low dimensional spaces, the old method of Nelder and Mead is still the most successful among those compared, while in spaces of higher dimensions, it is better to choose an algorithm based on quadratic modeling, such as NEWUOA or a quasi-Newton method. PMID:22779407
Bin-Slotted Hybrid Search Algorithm for Multiple RFID Arbitration
Bong-soo Kang; Ji-yoon Kim; Jeongwoo Jwa; Doo-yeong Yang
In this paper, bin-slotted hybrid search algorithm (BHS) combined slotted ALOHA procedure with bin-tree procedure is proposed and analyzed. Also, the performance of proposed anti-collision algorithm is evaluated as comparing the BHS algorithm with a standard bin-slotted algorithm (BSA) through the simulation. The performance of the proposed BHS algorithm is improved by dynamically identifying the collided-bit position and the collided
A Comprehensive Review of Swarm Optimization Algorithms
Many swarm optimization algorithms have been introduced since the early 60�s, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches. PMID:25992655
A comprehensive review of swarm optimization algorithms.
Ab Wahab, Mohd Nadhir; Nefti-Meziani, Samia; Atyabi, Adham
Many swarm optimization algorithms have been introduced since the early 60's, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches. PMID:25992655
Technology Transfer Automated Retrieval System (TEKTRAN)
The objective of this research was the development of a method that integrated a data envelopment analysis economic model of production with a biophysical model, with optimization over multiple objectives. We specified a hybrid genetic algorithm using DEA as a local search method, and NSGA-II for c...
An effective hybrid PSO-based algorithm for flow shop scheduling with limited buffers
Bo Liu; Ling Wang; Yi-Hui Jin
In this paper, an effective hybrid algorithm based on particle swarm optimization (HPSO) is proposed for permutation flow shop scheduling problem (PFSSP) with the limited buffers between consecutive machines to minimize the maximum completion time (i.e., makespan). First, a novel encoding scheme based on random key representation is developed, which converts the continuous position values of particles in PSO to
A Hybrid Multiobjective Evolutionary Algorithm For Solving Truck And Trailer Vehicle Routing Problems
Coello, Carlos A. Coello
A Hybrid Multiobjective Evolutionary Algorithm For Solving Truck And Trailer Vehicle Routing. A model for this truck and trailer vehicle routing problem (TTVRP) is first constructed in the paper and availability of trailers. To solve such a multiobjective and multi-modal combinatorial optimization problem
Hybrid GA-SA Algorithms for Reducing Energy Consumption in Embedded Systems
Schott, Ren� - Institut de Math�matiques �lie Cartan, Universit� Henri Poincar�
of optimal energy usage in real-time embedded systems from a software point of view, working on the memoryHybrid GA-SA Algorithms for Reducing Energy Consumption in Embedded Systems Maha IDRISSI AOUAD Vandoeuvre-L`es-Nancy, France. Email: email@example.com Abstract--Reducing energy consumption in embedded
Parameter identification of chaotic systems by hybrid Nelder-Mead simplex search and differential evolution algorithm
Ling Wang; Ye Xu; Lingpo Li
Parameter identification of chaotic systems is an important issue in nonlinear science and has attracted increasing interest from a variety of research and application fields. Essentially, parameter identification can be formulated as a multi-dimensional optimization problem. By combining differential evolution (DE) and Nelder�Mead (NM) simplex search, an effective hybrid algorithm named NMDE is proposed in this paper. By suitably fusing
A Hybrid Simplex Multi-objective Evolutionary Algorithm Based on Preference Order Ranking
Guo Xiaofang
It could be concluded that all multi-objective evolutionary algorithms draw their strength from two aspects: convergence and diversity. In order to achieve these goals, This paper proposes a hybrid methods that combines GA with simplex search method for multi-objective optimization using preference order ranking. Preference order ranking is used as fitness assignment methodology to accelerate the performance of convergence, especially
Design and Optimization of Future Hybrid and Electric Propulsion Systems
Paris-Sud XI, Universit� de
Design and Optimization of Future Hybrid and Electric Propulsion Systems: An Advanced Tool and Optimization of Future Hybrid and Electric Propulsion Systems: An Advanced Tool Integrated in a Complete syst�mes de propulsion hybride et �lectrique: un outil avanc� et int�gr� dans une cha�ne compl�te d�di�e �
Microarray temperature optimization using hybridization kinetics.
Blair, Steve; Williams, Layne; Bishop, Justin; Chagovetz, Alexander
In any microarray hybridization experiment, there are contributions at each probe spot due to the match and numerous mismatch target species (i.e., cross-hybridizations). One goal of temperature optimization is to minimize the contribution of mismatch species; however, achieving this goal may come at the expense of obtaining equilibrium reaction conditions. We employ two-component thermodynamic and kinetic models to study the trade-offs involved in temperature optimization. These models show that the maximum selectivity is achieved at equilibrium, but that the mismatch species controls the time to equilibrium via the competitive displacement mechanism. Also, selectivity is improved at lower temperatures. However, the time to equilibrium is also extended, so that greater selectivity cannot be achieved in practice. We also employ a two-color real-time microarray reader to experimentally demonstrate these effects by independently monitoring the match and mismatch species during multiplex hybridization. The only universal criterion that can be employed is to optimize temperature based upon attaining equilibrium reaction conditions. This temperature varies from one probe to another, but can be determined empirically using standard microarray experimentation methods. PMID:19381979
Gaco - a Hybrid Ant Colony Optimization Metaheuristic for the Dynamic Load-Balanced Clustering Problem in Ad Hoc Networks
Chin Kuan Ho; Hong Tat Ewe
We present the design of a novel hybrid genetic ant colony optimization (GACO) metaheuristic. Genetic ant colony optimization is designed to address the dynamic load-balanced clustering problem formulated from ad hoc networks. The main algorithm in GACO is ACO. Whenever an environment change occurs (i.e., nodes move), it makes use of a genetic algorithm to quickly achieve adaptation by refocusing
Optimal design of a PV-diesel hybrid system for electrification of an isolated island�Sandwip in Bangladesh using genetic algorithm
BK Bala; Saiful Azam Siddique
It is not cost effective or feasible to extend a centralized power grid to islands and other isolated communities. Decentralized renewable energy sources are alternatives. Among these alternatives are hybrid photovoltaic systems which combine solar photovoltaic energy with other renewable energy sources like wind. A diesel backup system can be used when PV system fails to satisfy the load and
Combining genetic optimisation with hybrid learning algorithm for radial basis
Hefei Institute of Intelligent Machines
hidden neuron. GA and HLA: A general framework for the genetic algorithm (GA) has been described in [6Combining genetic optimisation with hybrid learning algorithm for radial basis function neural neural networks (RBFNN) is proposed. A genetic algorithm initially optimises the parameters of the RBFNN
Hybrid evolutionary algorithms based on PSO and GA
X. H. Shi; Y. H. Lu; C. G. Zhou; H. P. Lee; W. Z. Lin; Y. C. Liang
Inspired by the idea of genetic algorithm, we propose two hybrid evolutionary algorithms based on PSO and GA methods through crossing over the PSO and GA algorithms. The main ideas of the two proposed methods are to integrate PSO and GA methods in parallel and series forms respectively. Simulations for a series of benchmark test functions show that both of
Fault diagnosis of rotating machinery based on a new hybrid clustering algorithm
Yaguo Lei; Zhengjia He; Yanyang Zi; Qiao Hu
A new hybrid clustering algorithm based on a three-layer feed forward neural network (FFNN), a distribution density function,\\u000a and a cluster validity index, is presented in this paper. In this algorithm, both feature weighting and sample weighting are\\u000a considered, and an optimal cluster number is automatically determined by the cluster validity index. Feature weights are learnt\\u000a via FFNN based on
Hybrid Non-dominated Sorting Differential Evolutionary Algorithm with Nelder-Mead
Xiang Zhong; Wenhui Fan; Jinbiao Lin; Zuozhi Zhao
Non-dominated sorting has been widely applied in many multi-objective evolutionary algorithms, such as NSGA, NSGA-II and NSDE. This paper proposes a novel multi-objective optimization algorithm based on Nelder-Mead (NM) simplex method and non-dominated sorting (NS) approach, NS-simplex. It utilizes NS in simplex procedures and accelerates local search speed. Based on NS-simplex and NSDE, a hybrid technique called NSSDE is proposed,
Optimal multi-thresholding using a hybrid optimization approach
Erwie Zahara; Shu-kai S. Fan; Du-ming Tsai
The Otsu�s method has been proven as an efficient method in image segmentation for bi-level thresholding. However, this method is computationally intensive when extended to multi-level thresholding. In this paper, we present a hybrid optimization scheme for multiple thresholding by the criteria of (1) Otsu�s minimum within-group variance and (2) Gaussian function fitting. Four example images are used to test
A Hybrid Constructive Algorithm for Single-Layer Feedforward Networks Learning.
Wu, Xing; Rozycki, Pawel; Wilamowski, Bogdan M
Single-layer feedforward networks (SLFNs) have been proven to be a universal approximator when all the parameters are allowed to be adjustable. It is widely used in classification and regression problems. The SLFN learning involves two tasks: determining network size and training the parameters. Most current algorithms could not be satisfactory to both sides. Some algorithms focused on construction and only tuned part of the parameters, which may not be able to achieve a compact network. Other gradient-based optimization algorithms focused on parameters tuning while the network size has to be preset by the user. Therefore, trial-and-error approach has to be used to search the optimal network size. Because results of each trial cannot be reused in another trial, it costs much computation. In this paper, a hybrid constructive (HC)algorithm is proposed for SLFN learning, which can train all the parameters and determine the network size simultaneously. At first, by combining Levenberg-Marquardt algorithm and least-square method, a hybrid algorithm is presented for training SLFN with fixed network size. Then,with the hybrid algorithm, an incremental constructive scheme is proposed. A new randomly initialized neuron is added each time when the training entrapped into local minima. Because the training continued on previous results after adding new neurons, the proposed HC algorithm works efficiently. Several practical problems were given for comparison with other popular algorithms. The experimental results demonstrated that the HC algorithm worked more efficiently than those optimization methods with trial and error, and could achieve much more compact SLFN than those construction algorithms. PMID:25216485
A hybrid search algorithm with Hopfield neural network and Genetic algorithm for solving traveling salesman problem
Gohar Vahdati; Sima Yaghoubian Ghouchani; Mahdi Yaghoobi
In this paper, a hybrid search algorithm with Hopfield neural network (HNN) and Genetic algorithm (GA) is proposed. The HNN method is first used to generate valid solutions which are considered as solutions for initial population of genetic algorithm. Then, GA is used to perform exploitation around the best solution at each evaluation. The proposed algorithm has both the advantages
Approximate algorithms for Space Station Maneuver Optimization�
Mur-Dongil, Andres
APPROXIMATE ALGORITHMS FOR SPACE STATION MANEUVER OPTIMIZATION A Thesis by ANDRE S MUR-DONGIL Submitted to the OAice of Graduate Studies of Texas ARM University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE... August 1998 Major Subject: Aerospace Engineering APPROXIMATE ALGORITHMS FOR SPACE STATION MANEUVER OPTIMIZATION A Thesis by ANDRES MUR-DONGIL Submitted to Texas A&M University in partial fulfillment of the requirements for the degree of MASTER...
Algorithms for optimizing hydropower system operation
Jan C. Grygier; Jery R. Stedinger
Successive liner programming, an optimal control algorithm, and a combination of linear programming and dynamic programming (LP-DP) are employed to optimize the operation of multireservoir hydrosystems given a deterministic inflow forecast. The algorithm maximize the value of energy produced at on-peak rates, plus the estimated value of water remaining in storage at the end of the 12-month planning period. The
Adaptive Cuckoo Search Algorithm for Unconstrained Optimization
Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm (CSA) is performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster convergence to the global optimal solutions. The feasibility of the proposed algorithm is validated against benchmark optimization functions, where the obtained results demonstrate a marked improvement over the standard CSA, in all the cases. PMID:25298971
Hybrid Optimization Schemes for Quantum Control
Michael H Goerz; K. Birgitta Whaley; Christiane P Koch
Optimal control theory is a powerful tool for solving control problems in quantum mechanics, ranging from the control of chemical reactions to the implementation of gates in a quantum computer. Gradient-based optimization methods are able to find high fidelity controls, but require considerable numerical effort and often yield highly complex solutions. We propose here to employ a two-stage optimization scheme to significantly speed up convergence and achieve simpler controls. The control is initially parametrized using only a few free parameters, such that optimization in this pruned search space can be performed with a simplex method. The result, considered now simply as an arbitrary function on a time grid, is the starting point for further optimization with a gradient-based method that can quickly converge to high fidelities. We illustrate the success of this hybrid technique by optimizing a holonomic phasegate for two superconducting transmon qubits coupled with a shared transmission line resonator, showing that a combination of Nelder-Mead simplex and Krotov's method yields considerably better results than either one of the two methods alone.
Ant Algorithms Solve Difficult Optimization Problems
Libre de Bruxelles, Universit�
Ant Algorithms Solve Difficult Optimization Problems Marco Dorigo IRIDIA Universit�e Libre de Bruxelles 50 Avenue F. Roosevelt B-1050 Brussels, Belgium firstname.lastname@example.com Abstract. The ant algorithms research field builds on the idea that the study of the behavior of ant colonies or other social insects
Finding Tradeoffs by Using Multiobjective Optimization Algorithms
Shigeru Obayashi; Daisuke Sasaki; Akira Oyama
The objective of the present study is to demonstrate performances of Evolutionary Algorithms (EAs) and conventional gradient-based methods for finding Pareto fronts. The multiobjective optimization algorithms are applied to analytical test problems as well as to the real-world problems of a compressor design. The comparison results clearly indicate the superiority of EAs in finding tradeoffs.
Reservoir Operation by Ant Colony Optimization Algorithms
M. R. Jalali
In this paper, ant colony optimization (ACO) algorithms are proposed for reservoir operation. Through a collection of cooperative agents called ants, the near optimum solution to the reservoir operation can be effectively achieved. To apply ACO algorithms, the problem is approached by considering a finite horizon with a time series of inflow, classifying the reservoir volume to several intervals, and
Statistical analysis of optimization algorithms with R
Thomas Bartz-Beielstein; Mike Preu�; Martin Zaefferer
Based on experiences from several (rather theoretical) tutorials and workshops devoted to the experimental analysis of algorithms at the world's leading conferences in the field of Computational Intelligence, a practical, hands-on tutorial for the statistical analysis of optimization algorithms is presented. This tutorial -demonstrates how to analyze results from real experimental studies, e.g., experimental studies in EC -item gives a
An Improved Algorithm for Hydropower Optimization
K. K. Reznicek; S. P. Simonovic
A new algorithm named energy management by successive linear programming (EMSLP) was developed to solve the optimization problem of the hydropower system operation. The EMSLP algorithm has two iteration levels: at the first level a stable solution is sought, and at the second the interior of the feasible region is searched to improve the objective function whenever its value decreases.
A Modified Particle Swarm Optimizer Algorithm
Yang Guangyou
This paper presented a modified particle swarm optimizer algorithm (MPSO). The aggregation degree of the particle swarm was introduced. The particles' diversity was improved through periodically monitoring aggregation degree of the particle swarm. On the later development of the PSO algorithm, it has been taken strategy of the Gaussian mutation to the best particle's position, which enhanced the particles' capacity
Optimization of multi-pass milling using parallel genetic algorithm and parallel genetic simulated annealing
Z. G. Wang; M. Rahman; Y. S. Wong; J. Sun
This paper presents an approach to select the optimal machining parameters for multi-pass milling. It is based on two recent approaches, genetic algorithm (GA) and simulated annealing (SA), which have been applied to many difficult combinatorial optimization problems with certain strengths and weaknesses. In this paper, a hybrid of GA and SA (GSA) is presented to use the strengths of
Social Emotional Optimization Algorithm for Nonlinear Constrained Optimization Problems
NASA Astrophysics Data System (ADS)
Xu, Yuechun; Cui, Zhihua; Zeng, Jianchao
Nonlinear programming problem is one important branch in operational research, and has been successfully applied to various real-life problems. In this paper, a new approach called Social emotional optimization algorithm (SEOA) is used to solve this problem which is a new swarm intelligent technique by simulating the human behavior guided by emotion. Simulation results show that the social emotional optimization algorithm proposed in this paper is effective and efficiency for the nonlinear constrained programming problems.
An Emotional Particle Swarm Optimization Algorithm
Yang Ge; Zhang Rubo
\\u000a This paper presents a modification of the particle swarm optimization algorithm (PSO) intended to introduce some psychology\\u000a factor of emotion into the algorithm. In the new algorithm, which is based on a simple perception and emotion psychology model,\\u000a each particle has its own feeling and reaction to the current position, and it also has specified emotional factor towards\\u000a the sense
Hybrid swarm intelligence optimization approach for optimal data storage position identification in wireless sensor networks.
Mohanasundaram, Ranganathan; Periasamy, Pappampalayam Sanmugam
The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches. PMID:25734182
Hybrid Swarm Intelligence Optimization Approach for Optimal Data Storage Position Identification in Wireless Sensor Networks
Mohanasundaram, Ranganathan; Periasamy, Pappampalayam Sanmugam
The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches. PMID:25734182
Method of mechanism synthesis by hybrid genetic algorithm�
O'Neil, Robert Anthony
METHOD OF MECHANISM SYNTHESIS BY HYBRID GENETIC ALGORITHM A Thesis by ROBERT ANTHONY O' NEIL Jr. Submitted to the Office of Graduate Studies of Texas A8 M University in the partial fulfillment of the requirements for the degree of MASTER... OF SCIENCE December 1999 Major Subject: Mechanical Engineering Method of Mechanism Synthesis by Hybrid Genetic Algorithm A Thesis by ROBERT ANTHONY O' NEIL Jr. Submitted to Texas A8 M University in partial fulfillment of the requirements...
Solving the Course Timetabling Problem with a Hybrid Heuristic Algorithm
Hao, Jin-Kao
known results on two problem formulations. Keywords: Timetabling, hybrid heuristic, tabu searchSolving the Course Timetabling Problem with a Hybrid Heuristic Algorithm Zhipeng L�u1,2 and Jin, iterated local search, constraint solving. 1 Introduction In recent decades, timetabling has become an area
Randomized Parallel Algorithms in Optimization Stephen Wright
central memory, evalute g := fik (x); 3 for nonzero components gv do xv xv - gv ; Wright (UWRandomized Parallel Algorithms in Optimization Stephen Wright University of Wisconsin-Madison July 2013 Wright (UW-Madison) Random Parallel Optimization July 2013 1 / 52 #12;Collaborators @ UW
Graph algorithms for clock schedule optimization
Narendra V. Shenoy; Robert K. Brayton; Alberto L. Sangiovanni-Vincentelli
Performance driven synthesis of sequential circuits relies on techniquessuch as optimal clocking, retiming and resynthesis. In this paper we address the optimal clockingproblem and demonstrate that it is reducible to a parametric shortest path problem. We use constraints that take into account both the short and long paths. The main contributions are efjicient graph algorithms to solve the set of
A data locality optimizing algorithm
Monica S. Lam
This paper proposes an algorithm that improves the locality of a loop nest by transforming the code via interchange,reversal, skewing and tiling. The loop transformation rrlgorithm is based on two concepts: a mathematical formulation of reuse and locality, and a loop transformation theory that unifies the various transforms as unimodular matrix tmnsfonnations.The algorithm haa been implemented in the SUIF (Stanford
A data locality optimizing algorithm
Michael E. Wolf; Monica S. Lam
This paper proposes an algorithm that improves the locality of a loop nest by transforming the code via interchange, reversal, skewing and tiling. The loop transformation rrlgorithm is based on two concepts: a mathematical formulation of reuse and locality, and a loop transformation theory that unifies the various transforms as unimodular matrix tmnsfonnations. The algorithm haa been implemented in the
Algorithms for optimal dyadic decision trees
Hush, Don [Los Alamos National Laboratory; Porter, Reid [Los Alamos National Laboratory
A new algorithm for constructing optimal dyadic decision trees was recently introduced, analyzed, and shown to be very effective for low dimensional data sets. This paper enhances and extends this algorithm by: introducing an adaptive grid search for the regularization parameter that guarantees optimal solutions for all relevant trees sizes, revising the core tree-building algorithm so that its run time is substantially smaller for most regularization parameter values on the grid, and incorporating new data structures and data pre-processing steps that provide significant run time enhancement in practice.
Two stochastic optimization algorithms applied to nuclear reactor core design
Wagner F. Sacco; Cassiano R. E. de oliveira; Cl�udio M. N. A. Pereira
Two stochastic optimization algorithms conceptually similar to Simulated Annealing are presented and applied to a core design optimization problem previously solved with Genetic Algorithms. The two algorithms are the novel Particle Collision Algorithm (PCA), which is introduced in detail, and Dueck's Great Deluge Algorithm (GDA). The optimization problem consists in adjusting several reactor cell parameters, such as dimensions, enrichment and
Optimal Hops-Based Adaptive Clustering Algorithm
NASA Astrophysics Data System (ADS)
Xuan, Xin; Chen, Jian; Zhen, Shanshan; Kuo, Yonghong
This paper proposes an optimal hops-based adaptive clustering algorithm (OHACA). The algorithm sets an energy selection threshold before the cluster forms so that the nodes with less energy are more likely to go to sleep immediately. In setup phase, OHACA introduces an adaptive mechanism to adjust cluster head and load balance. And the optimal distance theory is applied to discover the practical optimal routing path to minimize the total energy for transmission. Simulation results show that OHACA prolongs the life of network, improves utilizing rate and transmits more data because of energy balance.
The research of virtual reality hybrid collision detection algorithm
NASA Astrophysics Data System (ADS)
Yang, Benchen
This paper presents an algorithm of hybrid collision detection. Firstly, the algorithm establishes sphere and OBB level bounding box for every model in virtual scene and then uses intersect test of sphere bounding box to exclude not intersect model. Between two model of maybe the intersection, it uses not intersect part of intersect test excluding model of OBB bounding box, reducing PSO searching space to inside of the nodes which collisions occur. The Algorithm can exclude not intersect model quickly and avoid slowly and early maturity because of PSO target space bigger. Also, it reduces that level bounding box algorithm take a lot of memory and newer rate slow. At last, it certifies Hybrid Collision Detection Algorithm though laboratory and compared with based OBB and random collision detection algorithm which based on improved PSO algorithm.
Parallel Algorithms for Graph Optimization using Tree Decompositions
Sullivan, Blair D [ORNL; Weerapurage, Dinesh P [ORNL; Groer, Christopher S [ORNL
Although many $\\cal{NP}$-hard graph optimization problems can be solved in polynomial time on graphs of bounded tree-width, the adoption of these techniques into mainstream scientific computation has been limited due to the high memory requirements of the necessary dynamic programming tables and excessive runtimes of sequential implementations. This work addresses both challenges by proposing a set of new parallel algorithms for all steps of a tree decomposition-based approach to solve the maximum weighted independent set problem. A hybrid OpenMP/MPI implementation includes a highly scalable parallel dynamic programming algorithm leveraging the MADNESS task-based runtime, and computational results demonstrate scaling. This work enables a significant expansion of the scale of graphs on which exact solutions to maximum weighted independent set can be obtained, and forms a framework for solving additional graph optimization problems with similar techniques.
Parallel Algorithms for Graph Optimization using Tree Decompositions
Weerapurage, Dinesh P [ORNL; Sullivan, Blair D [ORNL; Groer, Christopher S [ORNL
Although many NP-hard graph optimization problems can be solved in polynomial time on graphs of bounded tree-width, the adoption of these techniques into mainstream scientific computation has been limited due to the high memory requirements of required dynamic programming tables and excessive running times of sequential implementations. This work addresses both challenges by proposing a set of new parallel algorithms for all steps of a tree-decomposition based approach to solve maximum weighted independent set. A hybrid OpenMP/MPI implementation includes a highly scalable parallel dynamic programming algorithm leveraging the MADNESS task-based runtime, and computational results demonstrate scaling. This work enables a significant expansion of the scale of graphs on which exact solutions to maximum weighted independent set can be obtained, and forms a framework for solving additional graph optimization problems with similar techniques.
Research of Particle Swarm Optimization algorithm based on Nelder-Mead simplex and its application on partial discharge parameter recognition
Shuo Xu; XiaoBing Zou; WeiLi Liu; XinXin Wang; HongLin Zhu; Tong Zhao
Particle Swarm Optimization (PSO), firstly presented in 1995, is mainly used in high-dimension optimization. Despite its wide use, PSO have a disadvantage of prematurity. This paper put forward a new Particle Swarm Optimization algorithm based on Nelder-Mead simplex algorithm to overcome the PSO's nature of prematurity and precision problems. Nelder-Mead simplex algorithm is hybrid into the process of PSO. The
Hybrid method for aerodynamic shape optimization in automotive industry
Dumas, Laurent
Hybrid method for aerodynamic shape optimization in automotive industry Freedeerique Muyl April 2003; accepted 4 June 2003 Abstract An aerodynamic shape optimization tool for complex industrial reasons, concerns car manufacturers. Consequently, the improvement of the aerodynamics of car shapes, more
An Efficient Chemical Reaction Optimization Algorithm for Multiobjective Optimization.
Bechikh, Slim; Chaabani, Abir; Said, Lamjed Ben
Recently, a new metaheuristic called chemical reaction optimization was proposed. This search algorithm, inspired by chemical reactions launched during collisions, inherits several features from other metaheuristics such as simulated annealing and particle swarm optimization. This fact has made it, nowadays, one of the most powerful search algorithms in solving mono-objective optimization problems. In this paper, we propose a multiobjective variant of chemical reaction optimization, called nondominated sorting chemical reaction optimization, in an attempt to exploit chemical reaction optimization features in tackling problems involving multiple conflicting criteria. Since our approach is based on nondominated sorting, one of the main contributions of this paper is the proposal of a new quasi-linear average time complexity quick nondominated sorting algorithm; therebymaking our multiobjective algorithm efficient from a computational cost viewpoint. The experimental comparisons against several other multiobjective algorithms on a variety of benchmark problems involving various difficulties show the effectiveness and the efficiency of this multiobjective version in providing a wellconverged and well-diversified approximation of the Pareto front. PMID:25373137
Optical surface measurement using phase retrieval hybrid algorithm based on diffraction angular spectrum theory
NASA Astrophysics Data System (ADS)
Feng, Liang; Zeng, Zhi-ge; Wu, Yong-qian
In order to test the high dynamic range error beyond one wavelength after the rough polish process, we design a phase retrieval hybrid algorithm based on diffraction angular spectrum theory. Phase retrieval is a wave front sensing method that uses the intensity distribution to reconstruct the phase distribution of optical field. Phase retrieval is established on the model of diffractive propagation and approach the real intensity distribution gradually. In this paper, we introduce the basic principle and challenges of optical surface measurement using phase retrieval, then discuss the major parts of phase retrieval: diffractive propagation and hybrid algorithm. The angular spectrum theory describes the diffractive propagation in the frequency domain instead of spatial domain, which simplifies the computation greatly. Through the theoretical analysis, the angular spectrum in discrete form is more effective when the high frequency part values less and the diffractive distance isn't far. The phase retrieval hybrid algorithm derives from modified GS algorithm and conjugate gradient method, aiming to solve the problem of phase wrapping caused by the high dynamic range error. In the algorithm, phase distribution is described by Zernike polynomials and the coefficients of Zernike polynomials are optimized by the hybrid algorithm. Simulation results show that the retrieved phase distribution and real phase distribution are quite contiguous for the high dynamic range error beyond ?.
A hybrid genetic algorithm for the job shop scheduling problem
Jos� Fernando Gon�alves; Jorge Jos� De Magalh�es Mendes; Maur??cio G. C. Resende
This paper presents a hybrid genetic algorithm for the Job Shop Scheduling problem. The chromosome representation of the problem is based on random keys. The schedules are constructed using a priority rule in which the priorities are defined by the genetic algorithm. Schedules are constructed using a procedure that generates parameterized active schedules. After a schedule is obtained a local
Nonlinear Hybrid Dynamical Systems: Modeling, Optimal Control, and Applications
Stryk, Oskar von
hybrid modeling scheme well-suited to the study of hybrid dynamical systems has inspired many researchers structure, nonlinear differential equations have recently been published [15,19,28,41]. These efforts wereNonlinear Hybrid Dynamical Systems: Modeling, Optimal Control, and Applications Martin Buss1
Feature Selection via Modified Gravitational Optimization Algorithm
NASA Astrophysics Data System (ADS)
Nabizadeh, Nooshin; John, Nigel
Feature selection is the process of selecting a subset of relevant and most informative features, which efficiently represents the input data. We proposed a feature selection algorithm based on n-dimensional gravitational optimization algorithm (NGOA), which is based on the principle of gravitational fields. The objective function of optimization algorithm is a non-linear function of variables, which are called masses and defined based on extracted features. The forces between the masses as well as their new locations are calculated using the value of the objective function and the values of masses. We extracted variety of features applying different wavelet transforms and statistical methods on FLAIR and T1-weighted MR brain images. There are two classes of normal and abnormal tissues. Extracted features are divided into groups of five features. The best feature is selected in each group using N-dimensional gravitational optimization algorithm and support vector machine classifier. Then the selected features from each group make several groups of five features again and so on till desired number of features is selected. The advantage of NGOA algorithm is that the possibility of being drawn into a local optimal solution is very low. The experimental results show that our method outperforms some standard feature selection algorithms on both real-data and simulated brain tumor data.
A Cuckoo Search Algorithm for Multimodal Optimization
Interest in multimodal optimization is expanding rapidly, since many practical engineering problems demand the localization of multiple optima within a search space. On the other hand, the cuckoo search (CS) algorithm is a simple and effective global optimization algorithm which can not be directly applied to solve multimodal optimization problems. This paper proposes a new multimodal optimization algorithm called the multimodal cuckoo search (MCS). Under MCS, the original CS is enhanced with multimodal capacities by means of (1) the incorporation of a memory mechanism to efficiently register potential local optima according to their fitness value and the distance to other potential solutions, (2) the modification of the original CS individual selection strategy to accelerate the detection process of new local minima, and (3) the inclusion of a depuration procedure to cyclically eliminate duplicated memory elements. The performance of the proposed approach is compared to several state-of-the-art multimodal optimization algorithms considering a benchmark suite of fourteen multimodal problems. Experimental results indicate that the proposed strategy is capable of providing better and even a more consistent performance over existing well-known multimodal algorithms for the majority of test problems yet avoiding any serious computational deterioration. PMID:25147850
Fast Hybrid PSO and Tabu Search Approach for Optimization of a Fuzzy Controller
Talbi, Nesrine
In this paper, a fuzzy controller type Takagi_Sugeno zero order is optimized by the method of hybrid Particle Swarm Optimization (PSO) and Tabu Search (TS). The algorithm automatically adjusts the membership functions of fuzzy controller inputs and the conclusions of fuzzy rules. At each iteration of PSO, we calculate the best solution and we seek the best neighbor by Tabu search, this operation minimizes the number of iterations and computation time while maintaining accuracy and minimum response time. We apply this algorithm to optimize a fuzzy controller for a simple inverted pendulum with three rules.
Algorithm selection in structural optimization
Clune, Rory P. (Rory Patrick)
Structural optimization is largely unused as a practical design tool, despite an extensive academic literature which demonstrates its potential to dramatically improve design processes and outcomes. Many factors inhibit ...
Source optimization using particle swarm optimization algorithm in photolithography
NASA Astrophysics Data System (ADS)
Wang, Lei; Li, Sikun; Wang, Xiangzhao; Yan, Guanyong; Yang, Chaoxing
In recent years, with the availability of freeform sources, source optimization has emerged as one of the key techniques for achieving higher resolution without increasing the complexity of mask design. In this paper, an efficient source optimization approach using particle swarm optimization algorithm is proposed. The sources are represented by pixels and encoded into particles. The pattern fidelity is adopted as the fitness function to evaluate these particles. The source optimization approach is implemented by updating the velocities and positions of these particles. The approach is demonstrated by using two typical mask patterns, including a periodic array of contact holes and a vertical line/space design. The pattern errors are reduced by 66.1% and 39.3% respectively. Compared with the source optimization approach using genetic algorithm, the proposed approach leads to faster convergence while improving the image quality at the same time. The robustness of the proposed approach to initial sources is also verified.
Optimal parallel quantum query algorithms
Stacey Jeffery; Frederic Magniez; Ronald de Wolf
We study the complexity of quantum query algorithms that make p queries in parallel in each timestep. This model is in part motivated by the fact that decoherence times of qubits are typically small, so it makes sense to parallelize quantum algorithms as much as possible. We show tight bounds for a number of problems, specifically Theta((n/p)^{2/3}) p-parallel queries for element distinctness and Theta((n/p)^{k/(k+1)} for k-sum. Our upper bounds are obtained by parallelized quantum walk algorithms, and our lower bounds are based on a relatively small modification of the adversary lower bound method, combined with recent results of Belovs et al. on learning graphs. We also prove some general bounds, in particular that quantum and classical p-parallel complexity are polynomially related for all total functions f when p is small compared to f's block sensitivity.
A hybrid fast Hankel transform algorithm for electromagnetic modeling
Anderson, W.L.
A hybrid fast Hankel transform algorithm has been developed that uses several complementary features of two existing algorithms: Anderson's digital filtering or fast Hankel transform (FHT) algorithm and Chave's quadrature and continued fraction algorithm. A hybrid FHT subprogram (called HYBFHT) written in standard Fortran-77 provides a simple user interface to call either subalgorithm. The hybrid approach is an attempt to combine the best features of the two subalgorithms to minimize the user's coding requirements and to provide fast execution and good accuracy for a large class of electromagnetic problems involving various related Hankel transform sets with multiple arguments. Special cases of Hankel transforms of double-order and double-argument are discussed, where use of HYBFHT is shown to be advantageous for oscillatory kernal functions. -Author
Optimal Design of a Hybrid Electric Powertrain System
R. S. Krishnamachari; P. Y. Papalambros
Optimal design of an electric hybrid powertrain system using a decomposition-based approach is presented. In this approach, a general system design problem is first formulated without specifying objectives. The mathematical model is analyzed using partitioning techniques, and an optimal design problem that can be readily decomposed and solved using an appropriate coordination strategy is derived. Basic concepts for hybrid powertrains
A Hybrid Monkey Search Algorithm for Clustering Analysis
Chen, Xin; Zhou, Yongquan; Luo, Qifang
Clustering is a popular data analysis and data mining technique. The k-means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to fall into local optimum solution. In view of the disadvantages of the k-means method, this paper proposed a hybrid monkey algorithm based on search operator of artificial bee colony algorithm for clustering analysis and experiment on synthetic and real life datasets to show that the algorithm has a good performance than that of the basic monkey algorithm for clustering analysis. PMID:24772039
Hybrid ant colony optimization and visibility studies applied to a job-shop scheduling problem
J. Heinonen; F. Pettersson
A hybrid ant colony optimization (ACO) algorithm is applied to a well known job-shop scheduling problem: MT10 (Muth-Thompson). The ACO tries to preserve and improve existing solutions, and a postprocessing algorithm is applied to the tour of an ant upon its completion. Studies are performed to see what effect visibility has on the outcome with regards to the ACO part
An Optimal Drum Scheduling Algorithm
Suppose a set of N records must be read or written from a drum, fixed-head disk, or similar storage unit of a computer system. The records vary in length and are arbitrarily located on the surface of the drum. The problem considered here is to find an algorithm that schedules the processing of these records with the minimal total amount
MIP Models and Hybrid Algorithms for Simultaneous Job Splitting and Scheduling on Unrelated Parallel Machines
Ozmutlu, H. Cenk
We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms. PMID:24977204
Using Genetic Algorithms to Optimize ACS-TSP
Marcin L. Pilat; Tony White
We propose the addition of Genetic Algorithms to Ant Colony System (ACS) applied to improve performance. Two modifications are proposed and tested. The first algorithm is a hybrid between ACS-TSP and a Genetic Algorithm that encodes experimental variables in ants. The algorithm does not yield improved results but oers concepts that can be used to improve the ACO algorithm. The
Multilevel thresholding algorithm based on particle swarm optimization for image segmentation
Chen Wei; Fang Kangling
The Otsu method is a popular non-parametric method in image segmentation. However, the computation time grows exponentially with the number of thresholds when this method extended to multi-level thresholding. This paper presents a hybrid optimization scheme based on a self-adaptive particle swarm optimization algorithm for multilevel thresholding by the criteria of Otsu minimum within-group variance to render the optimal thresholding
Implementation and comparison of PSO-based algorithms for multi-modal optimization problems
NASA Astrophysics Data System (ADS)
Sriyanyong, Pichet; Lu, Haiyan
This paper aims to compare the global search capability and overall performance of a number of Particle Swarm Optimization (PSO) based algorithms in the context solving the Dynamic Economic Dispatch (DED) problem which takes into account the operation limitations of generation units such as valve-point loading effect as well as ramp rate limits. The comparative study uses six PSO-based algorithms including the basic PSO and hybrid PSO algorithms using a popular benchmark test IEEE power system which is 10-unit 24-hour system with non-smooth cost functions. The experimental results show that one of the hybrid algorithms that combines the PSO with both inertia weight and constriction factor, and the Gaussian mutation operator (CBPSO-GM) is promising in achieving the near global optimal of a non-linear multi-modal optimization problem, such as the DED problem under the consideration.
A Binary Particle Swarm Optimization Algorithm for Lot Sizing Problem
M. Fatih; Yun-Chia Liang
This paper presents a binary particle swarm optimization algorithm for the lot sizing problem. The problem is to find order quantities which will minimize the total ordering and holding costs of ordering decisions. Test problems are constructed randomly, and solved optimally by Wagner and Whitin Algorithm. Then a binary particle swarm optimization algorithm and a traditional genetic algorithm are coded
Chaotic Particle Swarm Optimization Algorithm for Traveling Salesman Problem
Zhenglei Yuan; Liliang Yang; Yaohua Wu; Li Liao; Guoqiang Li
In this paper, a novel algorithm based on particle optimization algorithm (PSO) and chaos optimization algorithm (COA) is presented to solve traveling salesman problem. Some new operators are proposed to overcome the difficulties of implementing PSO into solving the discreet problems. Meanwhile embedded with chaos optimization algorithm (COA) it can enhance particle's global searching ability so as not to converge
A Simulative Bionic Intelligent Optimization Algorithm: Artificial Searching Swarm Algorithm and Its Performance Analysis
Tanggong Chen
In this paper, a novel optimization algorithm - artificial searching swarm algorithm (ASSA) is presented by analyzing the operating principle and uniform framework of the bionic intelligent optimization algorithm. ASSA simulates the process of solving optimal design problem to the process of searching optimal goal by searching swarm with the set rules, and finds the optimal solution through the search
An Improved Algorithm for Hydropower Optimization
NASA Astrophysics Data System (ADS)
Reznicek, K. K.; Simonovic, S. P.
A new algorithm named energy management by successive linear programming (EMSLP) was developed to solve the optimization problem of the hydropower system operation. The EMSLP algorithm has two iteration levels: at the first level a stable solution is sought, and at the second the interior of the feasible region is searched to improve the objective function whenever its value decreases. The EMSLP algorithm has been tested using the Manitoba Hydro system data applied to a single reservoir system. To evaluate the performance of the algorithm the comparison has been made with the results obtained by the energy management and maintenance analysis (EMMA) program used in the Manitoba Hydro practice. The paper describes the EMSLP algorithm and presents the results of the comparison with EMMA.
Optimized Vertex Method and Hybrid Reliability
NASA Technical Reports Server (NTRS)
Smith, Steven A.; Krishnamurthy, T.; Mason, B. H.
A method of calculating the fuzzy response of a system is presented. This method, called the Optimized Vertex Method (OVM), is based upon the vertex method but requires considerably fewer function evaluations. The method is demonstrated by calculating the response membership function of strain-energy release rate for a bonded joint with a crack. The possibility of failure of the bonded joint was determined over a range of loads. After completing the possibilistic analysis, the possibilistic (fuzzy) membership functions were transformed to probability density functions and the probability of failure of the bonded joint was calculated. This approach is called a possibility-based hybrid reliability assessment. The possibility and probability of failure are presented and compared to a Monte Carlo Simulation (MCS) of the bonded joint.
Multi-phase generalization of the particle swarm optimization algorithm
Buthainah Al-kazemi; Chilukuri K. Mohan
Multi-phase particle swarm optimization is a new algorithm to be used for discrete and continuous problems. In this algorithm, different groups of particles have trajectories that proceed with differing goals in different phases of the algorithm. On several benchmark problems, the algorithm outperforms standard particle swarm optimization, genetic algorithm, and evolution programming
A hybrid metaheuristic DE/CS algorithm for UCAV three-dimension path planning.
Wang, Gaige; Guo, Lihong; Duan, Hong; Wang, Heqi; Liu, Luo; Shao, Mingzhen
Three-dimension path planning for uninhabited combat air vehicle (UCAV) is a complicated high-dimension optimization problem, which primarily centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. A new hybrid metaheuristic differential evolution (DE) and cuckoo search (CS) algorithm is proposed to solve the UCAV three-dimension path planning problem. DE is applied to optimize the process of selecting cuckoos of the improved CS model during the process of cuckoo updating in nest. The cuckoos can act as an agent in searching the optimal UCAV path. And then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic CS. The realization procedure for this hybrid metaheuristic approach DE/CS is also presented. In order to make the optimized UCAV path more feasible, the B-Spline curve is adopted for smoothing the path. To prove the performance of this proposed hybrid metaheuristic method, it is compared with basic CS algorithm. The experiment shows that the proposed approach is more effective and feasible in UCAV three-dimension path planning than the basic CS model. PMID:23193383
The particle swarm optimization algorithm in size and shape optimization
P. C. Fourie; A. A. Groenwold
. ��Shape and size optimization problems instructural design are addressed using the particle swarm optimization algorithm (PSOA).\\u000a In our implementation of the PSOA, the social behaviour of birds is mimicked. Individual birds exchange information about\\u000a their position, velocity and fitness, and the behaviour of the flock is then influenced to increase the probability of migration\\u000a to regions of high fitness.
Interior search algorithm (ISA): a novel approach for global optimization.
Gandomi, Amir H
This paper presents the interior search algorithm (ISA) as a novel method for solving optimization tasks. The proposed ISA is inspired by interior design and decoration. The algorithm is different from other metaheuristic algorithms and provides new insight for global optimization. The proposed method is verified using some benchmark mathematical and engineering problems commonly used in the area of optimization. ISA results are further compared with well-known optimization algorithms. The results show that the ISA is efficiently capable of solving optimization problems. The proposed algorithm can outperform the other well-known algorithms. Further, the proposed algorithm is very simple and it only has one parameter to tune. PMID:24785823
Optimal energy management in series hybrid electric vehicles
A. Brahma; Y. Guezennec; G. Rizzoni
This paper deals with the optimization of the instantaneous electrical generation\\/electrical storage power split in series hybrid electric vehicles (SHEV). Optimal energy management is related to the optimization of the instantaneous generation\\/storage power split in SHEV. Previously, a power split type solution of the series hybrid energy management problem has been attempted using a rule-based approach. Our approach performs a
Genetic algorithms in truss topological optimization
P. Hajela; E. Lee
The present paper describes the use of a stochastic search procedure that is the basis of genetic algorithms, in developing near-optimal topologies of load-bearing truss structures. The problem addressed is one wherein the structural geometry is created from a specification of load conditions and available support points in the design space. The development of this geometry must satisfy kinematic stability
Optimization algorithm for compact slab lasers
Changqing Cao; Xiaodong Zeng; Yuying An
The pump structure greatly influences the characteristics of a diode side-pumped laser. To achieve high absorption efficiency and a homogeneous pump-beam distribution simultaneously, a systemic algorithm has been established to optimize the pump structure, where multiple reflections occur on the internal wall of the reflector inside the pump chamber. A novel design of an efficient, highly reliable, and good beam
Reactive power optimization by genetic algorithm
K. Iba
This paper presents a new approach to optimal reactive power planning based on a genetic algorithm. Many outstanding methods to this problem have been proposed in the past. However, most these approaches have the common defect of being caught to a local minimum solution. The integer problem which yields integer value solutions for discrete controllers\\/banks still remain as a difficult
Optimal Stochastic Approximation Algorithms for Strongly Convex ...
Jul 1, 2010 ... then SA algorithms became widely used in stochastic optimization (see, e.g., ... where X is a closed convex set in Euclidean space E, X(x) is a simple ..... Proof. The definition of u? and the fact V (�x,�) is a differentiable convex�...
Lightweight telescope structure optimized by genetic algorithm
Mikio Kurita; Hiroshi Ohmori; Masashi Kunda; Hiroaki Kawamura; Noriaki Noda; Takayuki Seki; Yuji Nishimura; Michitoshi Yoshida; Shuji Sato; Tetsuya Nagata
We designed the optics supporting structure (OSS) of a 3.8 m segmented mirror telescope by applying genetic algorithm optimization. The telescope is the first segmented mirror telescope in Japan whose primary mirror consists of 18 petal shaped segment mirrors. The whole mirror is supported by 54 actuators (3 actuators per each segment). In order to realize light-weight and stiff telescope
Optimizing continuous berth allocation by immune algorithm
Zhi-Hua Hu; Xiao-Long Han; Yi-Zhong Ding
The continuous berth allocation problem (BAPC) solves the BAP with continuous berth space and continuous time to optimize the utilization of space and time of the ports. A nonlinear programming (NLP) model is built and an immune algorithm (IA) is proposed to solve it. The effects of the number of vessels, the berth space length and the length of planning
Genetic Algorithms for Real Parameter Optimization
Alden H. Wright
This paper is concerned with the application of gen etic algorithms to optimization problems over several real parameters. It is shown that k-point crossover (for k small relative to the number of parameters) can be viewed as a crossover operation on the vector of parameters plus perturbations of some of the parameters. Mutation can also be co nsidered as a
Allauzen, Cyril
GENERALIZED OPTIMIZATION ALGORITHM FOR SPEECH RECOGNITION TRANSDUCERS Cyril Allauzen and Mehryar provide a common representation for the components of a speech recognition system. In previous work, we, determinization. However, not all weighted automata and transducers used in large- vocabulary speech recognition
Mohri, Mehryar
GENERALIZED OPTIMIZATION ALGORITHM FOR SPEECH RECOGNITION TRANSDUCERS Cyril Allauzen and Mehryar provide a common representation for the components of a speech recognition system. In previous work, we, determinization. However, not all weighted automata and transducers used in large� vocabulary speech recognition
Optimization Algorithms in Machine Learning Stephen Wright
Wright, Steve
/µ. Sometimes discuss convex quadratic f : f (x) = 1 2 xT Ax, where µI A LI. Stephen Wright (UWOptimization Algorithms in Machine Learning Stephen Wright University of Wisconsin-Madison NIPS Tutorial, 6 Dec 2010 Stephen Wright (UW-Madison) Optimization in Machine Learning NIPS Tutorial, 6 Dec 2010
Row-action Optimization Bregman's Algorithm
Sra, Suvrit
Row-action Optimization Methods Bregman's Algorithm Suvrit Sra email@example.com Department of Computer Sciences The University of Texas at Austin Austin, TX 78712 Suvrit Sra, Univ. of Texas at Austin Cyclically enforces one constraint at a time ( ai, x bi). Suvrit Sra, Univ. of Texas at Austin p.2/23 #12
Algorithm for fixed-range optimal trajectories
NASA Technical Reports Server (NTRS)
Lee, H. Q.; Erzberger, H.
An algorithm for synthesizing optimal aircraft trajectories for specified range was developed and implemented in a computer program written in FORTRAN IV. The algorithm, its computer implementation, and a set of example optimum trajectories for the Boeing 727-100 aircraft are described. The algorithm optimizes trajectories with respect to a cost function that is the weighted sum of fuel cost and time cost. The optimum trajectory consists at most of a three segments: climb, cruise, and descent. The climb and descent profiles are generated by integrating a simplified set of kinematic and dynamic equations wherein the total energy of the aircraft is the independent or time like variable. At each energy level the optimum airspeeds and thrust settings are obtained as the values that minimize the variational Hamiltonian. Although the emphasis is on an off-line, open-loop computation, eventually the most important application will be in an on-board flight management system.
Design Optimization of an Axial Fan Blade Through Multi-Objective Evolutionary Algorithm
Jin-Hyuk Kim; Jae-Ho Choi; Afzal Husain; Kwang-Yong Kim
This paper presents design optimization of an axial fan blade with hybrid multi-objective evolutionary algorithm (hybrid MOEA). Reynolds-averaged Navier-Stokes equations with shear stress transport turbulence model are discretized by the finite volume approximations and solved on hexahedral grids for the flow analyses. The validation of the numerical results was performed with the experimental data for the axial and tangential velocities.
G/SPLINES: A hybrid of Friedman's Multivariate Adaptive Regression Splines (MARS) algorithm with Holland's genetic algorithm
NASA Technical Reports Server (NTRS)
Rogers, David
G/SPLINES are a hybrid of Friedman's Multivariable Adaptive Regression Splines (MARS) algorithm with Holland's Genetic Algorithm. In this hybrid, the incremental search is replaced by a genetic search. The G/SPLINE algorithm exhibits performance comparable to that of the MARS algorithm, requires fewer least squares computations, and allows significantly larger problems to be considered.
Hybrid protection algorithms based on game theory in multi-domain optical networks
NASA Astrophysics Data System (ADS)
Guo, Lei; Wu, Jingjing; Hou, Weigang; Liu, Yejun; Zhang, Lincong; Li, Hongming
With the network size increasing, the optical backbone is divided into multiple domains and each domain has its own network operator and management policy. At the same time, the failures in optical network may lead to a huge data loss since each wavelength carries a lot of traffic. Therefore, the survivability in multi-domain optical network is very important. However, existing survivable algorithms can achieve only the unilateral optimization for profit of either users or network operators. Then, they cannot well find the double-win optimal solution with considering economic factors for both users and network operators. Thus, in this paper we develop the multi-domain network model with involving multiple Quality of Service (QoS) parameters. After presenting the link evaluation approach based on fuzzy mathematics, we propose the game model to find the optimal solution to maximize the user's utility, the network operator's utility, and the joint utility of user and network operator. Since the problem of finding double-win optimal solution is NP-complete, we propose two new hybrid protection algorithms, Intra-domain Sub-path Protection (ISP) algorithm and Inter-domain End-to-end Protection (IEP) algorithm. In ISP and IEP, the hybrid protection means that the intelligent algorithm based on Bacterial Colony Optimization (BCO) and the heuristic algorithm are used to solve the survivability in intra-domain routing and inter-domain routing, respectively. Simulation results show that ISP and IEP have the similar comprehensive utility. In addition, ISP has better resource utilization efficiency, lower blocking probability, and higher network operator's utility, while IEP has better user's utility.
Hybrid optimization setpoint strategy for slab reheating furnace temperature
Zhongjie Wang; Tianyou Chai; Shouping Guan; Cheng Shao
This paper investigated a hybrid optimization setpoint strategy for slab reheating furnace temperature. Due to the complexity of the process dynamics, the strategy is divided into two parts: steady state zone temperature optimization and its dynamic compensation. Simplex method is adopted in solving the optimization problem and PID regulation and expert experiences are applied to dynamic compensation procedure, the PID
Hybrid Heuristics for Optimizing Energy Consumption in Embedded Systems
Schott, Ren� - Institut de Math�matiques �lie Cartan, Universit� Henri Poincar�
the issue of optimal energy usage in real-time embedded systems from a software point of view, workingHybrid Heuristics for Optimizing Energy Consumption in Embedded Systems Maha IDRISSI AOUAD1 , Ren management, optimizations, Tabu Search. 1 Introduction Embedded systems become more and more energy greedy
PIBEA: Prospect Indicator Based Evolutionary Algorithm for Multiobjective Optimization Problems
Suzuki, Jun
PIBEA: Prospect Indicator Based Evolutionary Algorithm for Multiobjective Optimization Problems multiobjective optimization algorithm (EMOA) that uses a new quality indicator, called the prospect indicator, for parent selection and environmental selection operators. The prospect indicator measures the potential
Towards Enhancement of Performance of K-Means Clustering Using Nature-Inspired Optimization Algorithms
Deb, Suash; Yang, Xin-She
Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario. PMID:25202730
A Dynamic Hybrid Scheduling Algorithm with Clients' Departure for Impatient Clients in
Pinotti, Maria Cristina
. Our proposed hybrid scheduling strategy takes care of these effects to capture a real portrayalA Dynamic Hybrid Scheduling Algorithm with Clients' Departure for Impatient Clients scheduling to develop a new, practical, dynamic, hybrid scheduling strategy for heterogenous, asymmetric
A hybrid algorithm for far-field noise minimization
Markus P. Rumpfkeil; David W. Zingg
A general unsteady adjoint formulation is applied to a hybrid acoustic prediction algorithm to provide an efficient far-field noise minimization algorithm. Two-dimensional unsteady Navier�Stokes (NS) computations for calculating the properties of acoustic sources are combined with the Ffowcs Williams and Hawkings (FW�H) wave propagation formulation to calculate the resulting far-field noise. Two different time-marching methods, namely an implicit multi-stage and
A hybrid harmony search algorithm for MRI brain segmentation
Osama Moh�d Alia; Mandava Rajeswari; Mohd Ezane Aziz
Automatic magnetic resonance imaging (MRI) brain segmentation is a challenging problem that has received significant attention\\u000a in the field of medical image processing. In this paper, we present a new dynamic clustering algorithm based on the hybridization\\u000a of harmony search (HS) and fuzzy c-means to automatically segment MRI brain images in an intelligent manner. In our algorithm,\\u000a the capability of
Hybrid stochastic optimization algorithms with line search
Krejiæ, Nata�?a
, University of Novi Sad, Trg Dositeja Obradovi´ca 4, 21000 Novi Sad, Serbia, email:email@example.com, Research supported by Ministry of Science, Republic of Serbia, grant number 144006 2Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovi´ca 6, 21000 Novi Sad, Serbia 1 #12;in practical
A Hybrid Intelligent Learning Algorithm in MAS
Machine learning is a major branch of AI and a main research direction of multi-agent systems (MAS). With the emergence of various new complex systems, agent's individual ability and the system's intelligence level are urgently to be improved. Age nt learning methods and agent architecture are stud ied in this paper and a new hybrid intelligent learning al gorithm based
Hybrid Particle Swarm Optimization Approach for Optimal Distribution Generation Sizing and Allocation in Distribution Systems
M. F. AlHajri; M. R. AlRashidi; M. E. El-Hawary
This paper presents a novel particle swarm optimization based approach to optimally incorporate a distribution generator into a distribution system. The proposed algorithm combines particle swarm optimization with load flow algorithm to solve the problem in a single step, i.e. finding the best combination of location and size simultaneously. In the developed algorithm, the objective function to be minimized is
A reliable algorithm for optimal control synthesis
NASA Technical Reports Server (NTRS)
Vansteenwyk, Brett; Ly, Uy-Loi
In recent years, powerful design tools for linear time-invariant multivariable control systems have been developed based on direct parameter optimization. In this report, an algorithm for reliable optimal control synthesis using parameter optimization is presented. Specifically, a robust numerical algorithm is developed for the evaluation of the H(sup 2)-like cost functional and its gradients with respect to the controller design parameters. The method is specifically designed to handle defective degenerate systems and is based on the well-known Pade series approximation of the matrix exponential. Numerical test problems in control synthesis for simple mechanical systems and for a flexible structure with densely packed modes illustrate positively the reliability of this method when compared to a method based on diagonalization. Several types of cost functions have been considered: a cost function for robust control consisting of a linear combination of quadratic objectives for deterministic and random disturbances, and one representing an upper bound on the quadratic objective for worst case initial conditions. Finally, a framework for multivariable control synthesis has been developed combining the concept of closed-loop transfer recovery with numerical parameter optimization. The procedure enables designers to synthesize not only observer-based controllers but also controllers of arbitrary order and structure. Numerical design solutions rely heavily on the robust algorithm due to the high order of the synthesis model and the presence of near-overlapping modes. The design approach is successfully applied to the design of a high-bandwidth control system for a rotorcraft.
Stroke volume optimization: the new hemodynamic algorithm.
Johnson, Alexander; Ahrens, Thomas
Critical care practices have evolved to rely more on physical assessments for monitoring cardiac output and evaluating fluid volume status because these assessments are less invasive and more convenient to use than is a pulmonary artery catheter. Despite this trend, level of consciousness, central venous pressure, urine output, heart rate, and blood pressure remain assessments that are slow to be changed, potentially misleading, and often manifested as late indications of decreased cardiac output. The hemodynamic optimization strategy called stroke volume optimization might provide a proactive guide for clinicians to optimize a patient's status before late indications of a worsening condition occur. The evidence supporting use of the stroke volume optimization algorithm to treat hypovolemia is increasing. Many of the cardiac output monitor technologies today measure stroke volume, as well as the parameters that comprise stroke volume: preload, afterload, and contractility. PMID:25639574
Robust 2D/3D registration for fast-flexion motion of the knee joint using hybrid optimization.
Ohnishi, Takashi; Suzuki, Masahiko; Kobayashi, Tatsuya; Naomoto, Shinji; Sukegawa, Tomoyuki; Nawata, Atsushi; Haneishi, Hideaki
Previously, we proposed a 2D/3D registration method that uses Powell's algorithm to obtain 3D motion of a knee joint by 3D computed-tomography and bi-plane fluoroscopic images. The 2D/3D registration is performed consecutively and automatically for each frame of the fluoroscopic images. This method starts from the optimum parameters of the previous frame for each frame except for the first one, and it searches for the next set of optimum parameters using Powell's algorithm. However, if the flexion motion of the knee joint is fast, it is likely that Powell's algorithm will provide a mismatch because the initial parameters are far from the correct ones. In this study, we applied a hybrid optimization algorithm (HPS) combining Powell's algorithm with the Nelder-Mead simplex (NM-simplex) algorithm to overcome this problem. The performance of the HPS was compared with the separate performances of Powell's algorithm and the NM-simplex algorithm, the Quasi-Newton algorithm and hybrid optimization algorithm with the Quasi-Newton and NM-simplex algorithms with five patient data sets in terms of the root-mean-square error (RMSE), target registration error (TRE), success rate, and processing time. The RMSE, TRE, and the success rate of the HPS were better than those of the other optimization algorithms, and the processing time was similar to that of Powell's algorithm alone. PMID:23138929
Yang, Shengxiang
GENETIC ALGORITHM AND NEURAL NETWORK HYBRID APPROACH FOR JOB-SHOP SCHEDULING KAI ZHAO, SHENGXIANG (CSANN) hybrid approach for job-shop scheduling problems. In the hybrid approach, GA is used to iterate algorithm. Simulations have shown the valid performance of the proposed hybrid approach for job-shop
Wireless sensor network path optimization based on particle swarm algorithm
Xia Zhu; Yulin Zhang
This paper proposes a particle swarm optimization algorithm for Wireless Sensor Network (WSN) path optimization. It designs and increases the mutation operator. This algorithm can find effective optimization of WSN routing, not only the solution quality is superior to genetic algorithm, but also increases in the success rate. In experimental results verified that proposed PSO-WSN intelligent method can escape from
Genetic Algorithms Compared to Other Techniques for Pipe Optimization
Angus R. Simpson; Graeme C. Dandy; Laurence J. Murphy
The genetic algorithm technique is a relatively new optimization tech- nique. In this paper we present a methodology for optimizing pipe networks using genetic algorithms. Unknown decision variables are coded as binary strings. We investigate a three-operator genetic algorithm comprising reproduction, crossover, and mutation. Results are compared with the techniques of complete enumeration and nonlinear programming. We apply the optimization
The Use of Genetic Algorithms in Multilayer Mirror Optimization
Hart, Gus
The Use of Genetic Algorithms in Multilayer Mirror Optimization Shannon Lunt R. S. Turley 2 Abstract We have applied the genetic algorithm to extreme ultraviolet (XUV) multilayer mirror optimization. We have adapted the genetic algorithm to design optimal bifunctional mirrors for the IMAGE
Global Optimization Algorithms for Training Product Unit Neural
Neumaier, Arnold
, and shows that particle swarm optimization, genetic algorithms and LeapFrog are e�?cient alternativesGlobal Optimization Algorithms for Training Product Unit Neural Networks A Ismaily and AP) is possibly the most popular optimization algorithm to train multilayer NNs. While GD has shown
Experimental Comparisons of Derivative Free Optimization Algorithms1
Paris-Sud XI, Universit� de
-ES), the Differential Evolution (DE) algorithm and Particle Swarm Optimizers (PSO) are compared experimentally on benchExperimental Comparisons of Derivative Free Optimization Algorithms1 A. Auger,, N. Hansen,, J. M-Box Optimization (BBO). 1 Invited Paper at the 8th International Symposium on Experimental Algorithms, June 3
Distribution Systems Reconfiguration using a modified particle swarm optimization algorithm
A. Y. Abdelaziz; F. M. Mohammed; S. F. Mekhamer; M. A. L. Badr
This paper presents the particle swarm optimization (PSO) algorithm for solving the optimal distribution system reconfiguration problem for power loss minimization. The PSO is a relatively new and powerful intelligence evolution algorithm for solving optimization problems. It is a population-based approach. The PSO is originally inspired from the social behavior of bird flocks and fish schools. The proposed PSO algorithm
DIRECT algorithm : A new definition of potentially optimal ...
algorithm encounters The algorithm converges to the global optimal function value, if the objective ..... Future work should be done on numerical tests to compare ... [5] D. E. Finkel, C. T. Kelley, New Analysis of the DIRECT Algorithm, Copper.
A Hybrid Algorithm for the Examination Timetabling Problem
Liam T. G. Merlot; Natashia Boland; Barry D. Hughes; Peter J. Stuckey
Examination timetabling is a well-studied combinatorial op- timization problem. We present a new hybrid algorithm for examination timetabling, consisting of three phases: a constraint programming phase to develop an initial solution, a simulated annealing phase to improve the quality of solution, and a hill climbing phase for further improvement. The examination timetabling problem at the University of Melbourne is introduced,
A Genetic Algorithm for Minimax Optimization Problems Jeffrey W. Herrmann
Herrmann, Jeffrey W.
A Genetic Algorithm for Minimax Optimization Problems Jeffrey W. Herrmann Department of Mechanical-space genetic algorithm as a general technique to solve minimax optimization problems. This algorithm maintains of applications. To illustrate its potential, we use the two-space genetic algorithm to solve a parallel machine
A fuzzy-controlled Hooke-Jeeves optimization algorithm
Deepak Sankar Somasundaram; Mohamed B. Trabia
This article presents an approach to enhance the Hooke-Jeeves optimization algorithm through the use of fuzzy logic. The Hooke-Jeeves algorithm, similar to many other optimization algorithms, uses predetermined fixed parameters. These parameters do not depend on the objective function values in the current search region. In the proposed algorithm, several fuzzy logic controllers are integrated at the various stages of
Learning Computer Programs with the Bayesian Optimization Algorithm
Fernandez, Thomas
of the Bayesian Optimization Algorithm (BOA), a probabilistic model building genetic algorithm, to the domain of program tree evolution. The new system, BOA programming (BOAP), improves significantly on previous algorithms, such as the (hierarchical) Bayesian Optimization Algorithm (BOA) [6]. BOA is asymptotically more
Multi Swarm and Multi Best particle swarm optimization algorithm
Junliang Li; Xinping Xiao
This paper proposes a novel particle swarm optimization algorithm: Multi-Swarm and Multi-Best particle swarm optimization algorithm. The novel algorithm divides initialized particles into several populations randomly. After calculating certain generations respectively, every population is combined into one population and continues to calculate until the stop condition is satisfied. At the same time, the novel algorithm updates particlespsila velocities and positions
A Survey on Evolutionary Algorithm Based Hybrid Intelligence in Bioinformatics
Li, Shan; Zhao, Xing-Ming
With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs) are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks. PMID:24729969
Power optimized optical links for hybrid access networks
P. A. Gamage; A. Nirmalathas; C. Lim; E. Wong; D. Novak; R. Waterhouse
We propose a new technique to optimize the power of baseband and wireless RF signals in hybrid access networks. The calculated values for both signals are validated with experimental measurements over 22.5 km of transmission.
A hybrid rendering algorithm for textile objects
Mingmin Zhang; Zhigeng Pan; Jiaoying Shi; Peng Wang; Qingfeng Mi
Although realistic textile rendering has been widely used in virtual garment and try-on systems, a robust method to simulate\\u000a textile with a realistic appearance and high fidelity is yet to be established. We propose to use a novel hybrid geometric-\\u000a and image-based rendering (GIBR) method to achieve photo realistic representation of textile products. The image-based technique,\\u000a with its radiance synthesis
Design of broadband metal nanosphere antenna arrays with a hybrid evolutionary algorithm.
Donelli, Massimo
This Letter presents a method for the design of metal nanosphere antenna arrays with broadband plasmonic field enhancement over the whole visible spectra. In particular, thanks to a customized hybrid genetic algorithm (HGA), the plasmonic near-field enhancement and the sidelobe level are maximized and minimized, respectively, by thinning the nanoarray, optimizing the elements spacing and dimensions. In particular the problem is recast as an optimization one by defining a suitable cost function, which is then minimized with the HGA. The obtained preliminary results demonstrate the potentialities of the proposed approach. PMID:23455082
Optimal neuro-fuzzy control of parallel hybrid electric vehicles
M. Mohebbi; M. Charkhgard; M. Farrokhi
In this paper an optimal method based on neuro-fuzzy for controlling parallel hybrid electric vehicles is presented. In parallel hybrid electric vehicles the required torque for driving and operating the onboard accessories is generated by a combination of internal combustion engine and an electric motor. The power sharing between the internal combustion engine and the electric motor is the key
Shuffled Frog Leaping Algorithm Based Optimal Reactive Power Flow
Qingzheng Li
Abstract-A new approach to ORPF (optimal reactive power flow) based on SFLA (shuffled frog leaping algorithm) is proposed. The algorithm approaches to solving ORPF problem are given. By applying the algorithm to dealing with IEEE 30-bus system, compared with the particle swarm optimization (PSO) algorithm and SGA(simple genetic algorithm),the experimental results show that the algorithm is indeed capable of obtaining
A numerical study of hybrid optimization methods for the molecular conformation problems
Meza, J.C.; Martinez, M.L.
An important area of research in computational biochemistry is the design of molecules for specific applications. The design of these molecules depends on the accurate determination of their three-dimensional structure or conformation. Under the assumption that molecules will settle into a configuration for which their energy is at a minimum, this design problem can be formulated as a global optimization problem. The solution of the molecular conformation problem can then be obtained, at least in principle, through any number of optimization algorithms. Unfortunately, it can easily be shown that there exist a large number of local minima for most molecules which makes this an extremely difficult problem for any standard optimization method. In this study, we present results for various optimization algorithms applied to a molecular conformation problem. We include results for genetic algorithms, simulated annealing, direct search methods, and several gradient methods. The major result of this study is that none of these standard methods can be used in isolation to efficiently generate minimum energy configurations. We propose instead several hybrid methods that combine properties of several local optimization algorithms. These hybrid methods have yielded better results on representative test problems than single methods.
Study on Improvement of Memory Cell Control in Hybridization of Immune Algorithm and Gradient Search for Multiple Solution Search
NASA Astrophysics Data System (ADS)
Hirotani, Yusuke; Ono, Satoshi; Nakayama, Shigeru
Many evolutionary computation methods have been proposed and applied to real world problems. But gradient methods are still effective in problems involving real-coded parameters. In addition, it is desirable to find not only an optimal solution but also plural optimal and semi-optimal solutions in most real world problems. Although a hybrid algorithm combining Immune Algorithm (IA) and Quasi-Newton method (QN) has been proposed for multiple solution search, its memory cell control sometimes fails to keep semi-optimal solutions whose evaluation value is not so high. In addition, because the hybrid algorithm applies QN only to memory cell candidates, QN can be used as local search operator only after global search by IA. This paper proposes an improved memory cell control which restricts existence of redundant memory cells, and a QN application method which uses QN even in early search stage. Experimental results have shown that the hybrid algorithm involving the proposed improvements can find optimal and semi-optimal solutions with high accuracy and efficiency even in high-dimensional multimodal functions involving epistasis.
Efficient algorithms for globally optimal trajectories
John N. Tsitsiklis
We present serial and parallel algorithms for solving a system of equations that arises from the discretization of the Hamilton-Jacobi equation associated to a trajectory optimization problem of the following type. A vehicle starts at a prespecified point xo and follows a unit speed trajectory x(t) inside a region in ℛm until an unspecified time T that the region is
Multidisciplinary design optimization using genetic algorithms
NASA Technical Reports Server (NTRS)
Unal, Resit
Multidisciplinary design optimization (MDO) is an important step in the conceptual design and evaluation of launch vehicles since it can have a significant impact on performance and life cycle cost. The objective is to search the system design space to determine values of design variables that optimize the performance characteristic subject to system constraints. Gradient-based optimization routines have been used extensively for aerospace design optimization. However, one limitation of gradient based optimizers is their need for gradient information. Therefore, design problems which include discrete variables can not be studied. Such problems are common in launch vehicle design. For example, the number of engines and material choices must be integer values or assume only a few discrete values. In this study, genetic algorithms are investigated as an approach to MDO problems involving discrete variables and discontinuous domains. Optimization by genetic algorithms (GA) uses a search procedure which is fundamentally different from those gradient based methods. Genetic algorithms seek to find good solutions in an efficient and timely manner rather than finding the best solution. GA are designed to mimic evolutionary selection. A population of candidate designs is evaluated at each iteration, and each individual's probability of reproduction (existence in the next generation) depends on its fitness value (related to the value of the objective function). Progress toward the optimum is achieved by the crossover and mutation operations. GA is attractive since it uses only objective function values in the search process, so gradient calculations are avoided. Hence, GA are able to deal with discrete variables. Studies report success in the use of GA for aircraft design optimization studies, trajectory analysis, space structure design and control systems design. In these studies reliable convergence was achieved, but the number of function evaluations was large compared with efficient gradient methods. Applicaiton of GA is underway for a cost optimization study for a launch-vehicle fuel-tank and structural design of a wing. The strengths and limitations of GA for launch vehicle design optimization is studied.
Hybrid extragradient proximal algorithm coupled with parametric ...
In order to solve (P) we can use the classical proximal point algorithm, PPA for short: given x0 ? H, and a ..... Proof. Fix any u ? H, using Lemma 2.2 and the inclusion gk ? ??k fk(zk), we have ...... n an euclidean space and let ?k ? 0, uk ? E.
Registration of range data using a hybrid simulated annealing and iterative closest point algorithm
The need to register data is abundant in applications such as: world modeling, part inspection and manufacturing, object recognition, pose estimation, robotic navigation, and reverse engineering. Registration occurs by aligning the regions that are common to multiple images. The largest difficulty in performing this registration is dealing with outliers and local minima while remaining efficient. A commonly used technique, iterative closest point, is efficient but is unable to deal with outliers or avoid local minima. Another commonly used optimization algorithm, simulated annealing, is effective at dealing with local minima but is very slow. Therefore, the algorithm developed in this paper is a hybrid algorithm that combines the speed of iterative closest point with the robustness of simulated annealing. Additionally, a robust error function is incorporated to deal with outliers. This algorithm is incorporated into a complete modeling system that inputs two sets of range data, registers the sets, and outputs a composite model.
Algorithms for optimizing CT fluence control
NASA Astrophysics Data System (ADS)
Hsieh, Scott S.; Pelc, Norbert J.
The ability to customize the incident x-ray fluence in CT via beam-shaping filters or mA modulation is known to improve image quality and/or reduce radiation dose. Previous work has shown that complete control of x-ray fluence (ray-by-ray fluence modulation) would further improve dose efficiency. While complete control of fluence is not currently possible, emerging concepts such as dynamic attenuators and inverse-geometry CT allow nearly complete control to be realized. Optimally using ray-by-ray fluence modulation requires solving a very high-dimensional optimization problem. Most optimization techniques fail or only provide approximate solutions. We present efficient algorithms for minimizing mean or peak variance given a fixed dose limit. The reductions in variance can easily be translated to reduction in dose, if the original variance met image quality requirements. For mean variance, a closed form solution is derived. The peak variance problem is recast as iterated, weighted mean variance minimization, and at each iteration it is possible to bound the distance to the optimal solution. We apply our algorithms in simulations of scans of the thorax and abdomen. Peak variance reductions of 45% and 65% are demonstrated in the abdomen and thorax, respectively, compared to a bowtie filter alone. Mean variance shows smaller gains (about 15%).
A Discrete Particle Swarm Optimization Algorithm for Single Machine Total Earliness and Tardiness Problem with a Common Due Date
Quan-Ke Pan; M. Fatih Tasgetiren; Yun-Chia Liang
In this paper, a discrete particle swarm optimization (DPSO) algorithm is presented to solve the single machine total earliness and tardiness penalties with a common due date. A modified version of HRM heuristic presented by Hino et al. in [7], here we call it M_HRM, is also presented to solve the problem. In addition, the DPSO algorithm is hybridized with
Optimizing coherent anti-Stokes Raman scattering by genetic algorithm controlled pulse shaping
NASA Astrophysics Data System (ADS)
Yang, Wenlong; Sokolov, Alexei
The hybrid coherent anti-Stokes Raman scattering (CARS) has been successful applied to fast chemical sensitive detections. As the development of femto-second pulse shaping techniques, it is of great interest to find the optimum pulse shapes for CARS. The optimum pulse shapes should minimize the non-resonant four wave mixing (NRFWM) background and maximize the CARS signal. A genetic algorithm (GA) is developed to make a heuristic searching for optimized pulse shapes, which give the best signal the background ratio. The GA is shown to be able to rediscover the hybrid CARS scheme and find optimized pulse shapes for customized applications by itself.
Parallel Hybrid Vehicle Optimal Storage System
NASA Technical Reports Server (NTRS)
Bloomfield, Aaron P.
A paper reports the results of a Hybrid Diesel Vehicle Project focused on a parallel hybrid configuration suitable for diesel-powered, medium-sized, commercial vehicles commonly used for parcel delivery and shuttle buses, as the missions of these types of vehicles require frequent stops. During these stops, electric hybridization can effectively recover the vehicle's kinetic energy during the deceleration, store it onboard, and then use that energy to assist in the subsequent acceleration.
Bell-Curve Based Evolutionary Optimization Algorithm
NASA Technical Reports Server (NTRS)
Sobieszczanski-Sobieski, J.; Laba, K.; Kincaid, R.
The paper presents an optimization algorithm that falls in the category of genetic, or evolutionary algorithms. While the bit exchange is the basis of most of the Genetic Algorithms (GA) in research and applications in America, some alternatives, also in the category of evolutionary algorithms, but use a direct, geometrical approach have gained popularity in Europe and Asia. The Bell-Curve Based Evolutionary Algorithm (BCB) is in this alternative category and is distinguished by the use of a combination of n-dimensional geometry and the normal distribution, the bell-curve, in the generation of the offspring. The tool for creating a child is a geometrical construct comprising a line connecting two parents and a weighted point on that line. The point that defines the child deviates from the weighted point in two directions: parallel and orthogonal to the connecting line, the deviation in each direction obeying a probabilistic distribution. Tests showed satisfactory performance of BCB. The principal advantage of BCB is its controllability via the normal distribution parameters and the geometrical construct variables.
The research on edge detection algorithm based on hybrid intelligence for color image
NASA Astrophysics Data System (ADS)
Li, Weiping; Li, Chunyu
Based on the approach of minimization of the cost function, this paper brings forward a kind of hybrid intelligent algorithm for image edge detection, which is based on the particle swarm and quanta evolution algorithm. The implementation of the hybrid intelligent algorithm is also discussed to do the edge detection. The simulation results show that the new algorithm has obtained the satisfied detection effect.
A Hybrid Genetic Algorithm for the Point to Multipoint Routing Problem with
Wainwright, Roger L.
A Hybrid Genetic Algorithm for the Point to Multipoint Routing Problem with Single Split Paths Words: Genetic Algorithm, Steiner Trees, Point to Multipoint Routing, Telecommunications Network to Multipoint Routing Problem with Single Split Paths. Our hybrid algorithm uses a genetic algorithm
Bayesian Optimization Algorithm for Multi-Objective Solutions: Application to Electric Equipment Configuration Problems in a Power Plant
Coello, Carlos A. Coello
Search (Tabu-BOA) to electric equipments configuration problems in a power plant. Tabu-BOA is a hybrid Optimization Algorithm(1) and Tabu Search(2)(3) (Tabu-BOA) in order to solve electric equipments configuration. For example, Khan et al have proposed a variation of BOA for multi-objective optimization problems(4
Intervals in evolutionary algorithms for global optimization
Patil, R.B.
Optimization is of central concern to a number of disciplines. Interval Arithmetic methods for global optimization provide us with (guaranteed) verified results. These methods are mainly restricted to the classes of objective functions that are twice differentiable and use a simple strategy of eliminating a splitting larger regions of search space in the global optimization process. An efficient approach that combines the efficient strategy from Interval Global Optimization Methods and robustness of the Evolutionary Algorithms is proposed. In the proposed approach, search begins with randomly created interval vectors with interval widths equal to the whole domain. Before the beginning of the evolutionary process, fitness of these interval parameter vectors is defined by evaluating the objective function at the center of the initial interval vectors. In the subsequent evolutionary process the local optimization process returns an estimate of the bounds of the objective function over the interval vectors. Though these bounds may not be correct at the beginning due to large interval widths and complicated function properties, the process of reducing interval widths over time and a selection approach similar to simulated annealing helps in estimating reasonably correct bounds as the population evolves. The interval parameter vectors at these estimated bounds (local optima) are then subjected to crossover and mutation operators. This evolutionary process continues for predetermined number of generations in the search of the global optimum.
Stochastic Optimal Control for Series Hybrid Electric Vehicles
Malikopoulos, Andreas [ORNL] [ORNL
Increasing demand for improving fuel economy and reducing emissions has stimulated significant research and investment in hybrid propulsion systems. In this paper, we address the problem of optimizing online the supervisory control in a series hybrid configuration by modeling its operation as a controlled Markov chain using the average cost criterion. We treat the stochastic optimal control problem as a dual constrained optimization problem. We show that the control policy that yields higher probability distribution to the states with low cost and lower probability distribution to the states with high cost is an optimal control policy, defined as an equilibrium control policy. We demonstrate the effectiveness of the efficiency of the proposed controller in a series hybrid configuration and compare it with a thermostat-type controller.
The Ordered Clustered Travelling Salesman Problem: A Hybrid Genetic Algorithm
Ahmed, Zakir Hussain
The ordered clustered travelling salesman problem is a variation of the usual travelling salesman problem in which a set of vertices (except the starting vertex) of the network is divided into some prespecified clusters. The objective is to find the least cost Hamiltonian tour in which vertices of any cluster are visited contiguously and the clusters are visited in the prespecified order. The problem is NP-hard, and it arises in practical transportation and sequencing problems. This paper develops a hybrid genetic algorithm using sequential constructive crossover, 2-opt search, and a local search for obtaining heuristic solution to the problem. The efficiency of the algorithm has been examined against two existing algorithms for some asymmetric and symmetric TSPLIB instances of various sizes. The computational results show that the proposed algorithm is very effective in terms of solution quality and computational time. Finally, we present solution to some more symmetric TSPLIB instances. PMID:24701148
Toward a Proof of Convergence for the Optimal Stepsize Algorithm
Keinan, Alon
Toward a Proof of Convergence for the Optimal Stepsize Algorithm Peter Frazier Advisor: Warren Powell Department of Operations Research and Financial Engineering Princeton University Wednesday May 10 algorithm's rate of convergence. The optimal stepsize algorithm (OSA) developed by George and Powell [2
Optimal Separable Algorithms to Compute the Reverse Euclidean Distance Transformation
Paris-Sud XI, Universit� de
Optimal Separable Algorithms to Compute the Reverse Euclidean Distance Transformation and Discrete. In this paper, we present time optimal algorithms to solve the reverse Euclidean distance transformation algorithms to compute the error-free Euclidean Distance Transformation (EDT) for d-dimensional binary images
Computationally efficient optimal power allocation algorithms for multicarrier communication systems
Brian S. Krongold; Kannan Ramchandran; Douglas L. Jones
We present an optimal, computationally efficient, integer-bit power allocation algorithm for discrete multitone modulation. Using efficient lookup table searches and a Lagrange-multiplier bisection search, our algorithm converges faster to the optimal solution than existing techniques and can replace the use of suboptimal methods because of its low computational complexity. Fast algorithms are developed for the data rate and performance margin
Computationally efficient optimal power allocation algorithm for multicarrier communication systems
Brian S. Krongold; Kannan Ramchandran; Douglas L. Jones
We present an optimal, efficient power allocation algorithm for discrete multitone modulation (DMT). Using efficient lookup table searches and a Lagrange multiplier bisection search, our algorithm converges much faster to the optimal solution than existing techniques and can replace the use of suboptimal methods because of its low computational complexity. A fast algorithm is developed and a pseudocode is provided
Genetic Algorithms for Combinatorial Optimization: The Assembly Line Balancing Problem
Ferris, Michael C.
Genetic Algorithms for Combinatorial Optimization: The Assembly Line Balancing Problem Edward J optimization. We consider the application of the genetic algorithm to a particular problem, the Assembly Line Balancing Problem. A general description of genetic algorithms is given, and their specialized use on our
Towards a Genetic Algorithm for Function Optimization Sonja Novkovic
Towards a Genetic Algorithm for Function Optimization Sonja Novkovic and Davor Sverko Abstract: This article analyses a version of genetic algorithm (GA, Holland 1975) designed for function optimization, such as non-coding segments, elitist selection and multiple crossover. Key words: Genetic algorithm, Royal
An Estimation of Distribution Particle Swarm Optimization Algorithm
Mudassar Iqbal; Marco Antonio Montes De Oca
In this paper we present an estimation of distribution par- ticle swarm optimization algorithm that borrows ideas from recent de- velopments in ant colony optimization which can be considered an es- timation of distribution algorithm. In the classical particle swarm opti- mization algorithm, particles exploit their individual memory to explore the search space. However, the swarm as a whole has
Optimal algorithms and lower partial moment: ex post results
David N. Nawrocki
Portofolio management in the finance literature has typically used optimization algorithms to determine security allocations within a portfolio in order to obtain the best trade-off between risk and return. These algorithms, despite some improvements, are restrictive in terms of an investor's risk aversion (utility function). Since individual investors have different levels of risk aversion, this paper proposes two portfolio-optimization algorithms
Optimizing Parametric BIST Using Bio-inspired Computing Algorithms
Nastaran Nemati; Amirhossein Simjour; Amirali Ghofrani; Zainalabedin Navabi
Optimizing the BIST configuration based on the characteristics of the design under test is a complicated and challenging work for test engineers. Since this problem has multiple optimization factors, trapping in local optimums is very plausible. Therefore, regular computing algorithms cannot efficiently resolve this problem and utilization of some algorithms is required. In this work, by applying genetic algorithm (GA)
Optimal control of trading algorithms: a general impulse control approach
Paris-Sud XI, Universit� de
inclusion of a new effect in the "optimal-control-oriented" original framework gave birth to a specificOptimal control of trading algorithms: a general impulse control approach Bruno Bouchard , Ngoc-day trading based on the control of trading algorithms. Given a generic parameterized algorithm, we control
The Leap-Frog Algorithm and Optimal Control: Theoretical Aspects
Noakes, Lyle
The Leap-Frog Algorithm and Optimal Control: Theoretical Aspects C. Yal#24;c#16;n Kaya firstname.lastname@example.com Abstract The Leap-Frog Algorithm was originally devised to #12;nd geodesics in connected complete with generalizing the mathematical rigour of the leap-frog algorithm to a class of optimal control problems
Gaussian swarm: a novel particle swarm optimization algorithm
Renato A. Krohling; Lehrstuhl Elektrische
In this paper, a novel particle swarm optimization algorithm based on the Gaussian probability distribution is proposed. The standard particle swarm optimization (PSO) algorithm has some parameters that need to be specified before using the algorithm, e.g., the accelerating constants c1 and c2, the inertia weight w, the maximum velocity Vmax, and the number of particles of the swarm. The
Constraint-Based School Timetabling Using Hybrid Genetic Algorithms
Tuncay Yigit
In this paper, a hybrid genetic algorithm (HGA) has been developed to solve the constraint-based school timetabling problem\\u000a (CB-STTP). HGA has a new operator called repair operator, in addition to standard crossover and mutation operators. A timetabling\\u000a tool has been developed for HGA to solve CB-STTP. The timetabling tool has been tested extensively using real-word data obtained\\u000a the Technical and
Testing trivializing maps in the Hybrid Monte Carlo algorithm
Georg P. Engel; Stefan Schaefer
We test a recent proposal to use approximate trivializing maps in a field theory to speed up Hybrid Monte Carlo simulations. Simulating the CP^{N-1} model, we find a small improvement with the leading order transformation, which is however compensated by the additional computational overhead. The scaling of the algorithm towards the continuum is not changed. In particular, the effect of the topological modes on the autocorrelation times is studied.
A hybrid algorithm for synchronous generator parameter estimation
Wei Chen; Qingwu Gong; Lidan Zhang; Huali Chen; Tao Wang
This paper presents a hybrid algorithm for parameter estimation of synchronous generator. For large-residual problems (i.e., f(x) is large or f(x) is severely nonlinear), the performance of the Gauss-Newton method and Levenberg-Marquardt method is usually poor, and the slow convergence even causes iteration emergence divergence. The Quasi-Newton method can superlinearly converge, but it is not robust in the global stage
An effective hybrid cuckoo search algorithm with improved shuffled frog leaping algorithm for 0-1 knapsack problems.
Feng, Yanhong; Wang, Gai-Ge; Feng, Qingjiang; Zhao, Xiang-Jun
An effective hybrid cuckoo search algorithm (CS) with improved shuffled frog-leaping algorithm (ISFLA) is put forward for solving 0-1 knapsack problem. First of all, with the framework of SFLA, an improved frog-leap operator is designed with the effect of the global optimal information on the frog leaping and information exchange between frog individuals combined with genetic mutation with a small probability. Subsequently, in order to improve the convergence speed and enhance the exploitation ability, a novel CS model is proposed with considering the specific advantages of L�vy flights and frog-leap operator. Furthermore, the greedy transform method is used to repair the infeasible solution and optimize the feasible solution. Finally, numerical simulations are carried out on six different types of 0-1 knapsack instances, and the comparative results have shown the effectiveness of the proposed algorithm and its ability to achieve good quality solutions, which outperforms the binary cuckoo search, the binary differential evolution, and the genetic algorithm. PMID:25404940
An Effective Hybrid Cuckoo Search Algorithm with Improved Shuffled Frog Leaping Algorithm for 0-1 Knapsack Problems
Wang, Gai-Ge; Feng, Qingjiang; Zhao, Xiang-Jun
An effective hybrid cuckoo search algorithm (CS) with improved shuffled frog-leaping algorithm (ISFLA) is put forward for solving 0-1 knapsack problem. First of all, with the framework of SFLA, an improved frog-leap operator is designed with the effect of the global optimal information on the frog leaping and information exchange between frog individuals combined with genetic mutation with a small probability. Subsequently, in order to improve the convergence speed and enhance the exploitation ability, a novel CS model is proposed with considering the specific advantages of L�vy flights and frog-leap operator. Furthermore, the greedy transform method is used to repair the infeasible solution and optimize the feasible solution. Finally, numerical simulations are carried out on six different types of 0-1 knapsack instances, and the comparative results have shown the effectiveness of the proposed algorithm and its ability to achieve good quality solutions, which outperforms the binary cuckoo search, the binary differential evolution, and the genetic algorithm. PMID:25404940
An evolutionary game based particle swarm optimization algorithm
Wei-Bing Liu; Xian-Jia Wang
Particle swarm optimization (PSO) is an evolutionary algorithm used extensively. This paper presented a new particle swarm optimizer based on evolutionary game (EGPSO). We map particles� finding optimal solution in PSO algorithm to players� pursuing maximum utility by choosing strategies in evolutionary games, using replicator dynamics to model the behavior of particles. And in order to overcome premature convergence a
Drilling path optimization by the particle swarm optimization algorithm with global convergence characteristics
Guang-Yu Zhu; Wei-Bo Zhang
Drilling path optimization is one of the key problems in holes-machining. This paper presents a new approach to solve the drilling path optimization problem belonging to discrete space, based on the particle swarm optimization (PSO) algorithm. Since the standard PSO algorithm is not guaranteed to be global convergent or local convergent, based on the mathematical model, the algorithm is improved
Targeted 2D/3D registration using ray normalization and a hybrid optimizer
Dey, Joyoni; Napel, Sandy [Department of Radiology, University of Massachusetts Medical School, Worcester, Massachusetts 01655 (United States); Department of Radiology, Stanford University, Palo Alto, California (United States)
X-ray images are often used to guide minimally invasive procedures in interventional radiology. The use of a preoperatively obtained 3D volume can enhance the visualization needed for guiding catheters and other surgical devices. However, for intraoperative usefulness, the 3D dataset needs to be registered to the 2D x-ray images of the patient. We investigated the effect of targeting subvolumes of interest in the 3D datasets and registering the projections with C-arm x-ray images. We developed an intensity-based 2D/3D rigid-body registration using a Monte Carlo-based hybrid algorithm as the optimizer, using a single view for registration. Pattern intensity (PI) and mutual information (MI) were two metrics tested. We used normalization of the rays to address the problems due to truncation in 3D necessary for targeting. We tested the algorithm on a C-arm x-ray image of a pig's head and a 3D dataset reconstructed from multiple views of the C-arm. PI and MI were comparable in performance. For two subvolumes starting with a set of initial poses from +/-15 mm in x, from +/-3 mm (random), in y and z and +/-4 deg in the three angles, the robustness was 94% for PI and 91% for MI, with accuracy of 2.4 mm (PI) and 2.6 mm (MI), using the hybrid algorithm. The hybrid optimizer, when compared with a standard Powell's direction set method, increased the robustness from 59% (Powell) to 94% (hybrid). Another set of 50 random initial conditions from [+/-20] mm in x,y,z and [+/-10] deg in the three angles, yielded robustness of 84% (hybrid) versus 38% (Powell) using PI as metric, with accuracies 2.1 mm (hybrid) versus 2.0 mm (Powell)
Algorithms for Real-Time Game-Tree Search for Hybrid System Todd W. Neller
Neller, Todd W.
Algorithms for Real-Time Game-Tree Search for Hybrid System Control Todd W. Neller #3; email@example.com Abstract This paper describes four algorithms for real-time game-tree search for hybrid system control existing game-tree search tech- niques for real-time hybrid system control. We introduce the notion of an n
Modified artificial bee colony algorithm for reactive power optimization
NASA Astrophysics Data System (ADS)
Sulaiman, Noorazliza; Mohamad-Saleh, Junita; Abro, Abdul Ghani
Bio-inspired algorithms (BIAs) implemented to solve various optimization problems have shown promising results which are very important in this severely complex real-world. Artificial Bee Colony (ABC) algorithm, a kind of BIAs has demonstrated tremendous results as compared to other optimization algorithms. This paper presents a new modified ABC algorithm referred to as JA-ABC3 with the aim to enhance convergence speed and avoid premature convergence. The proposed algorithm has been simulated on ten commonly used benchmarks functions. Its performance has also been compared with other existing ABC variants. To justify its robust applicability, the proposed algorithm has been tested to solve Reactive Power Optimization problem. The results have shown that the proposed algorithm has superior performance to other existing ABC variants e.g. GABC, BABC1, BABC2, BsfABC dan IABC in terms of convergence speed. Furthermore, the proposed algorithm has also demonstrated excellence performance in solving Reactive Power Optimization problem.
Development and Optimization of Regularized Tomographic Reconstruction Algorithms Utilizing
Soatto, Stefano
in object or Fourier domain, which unavoidably introduces noise in the reconstructed images [4, 6]. A post1 Development and Optimization of Regularized Tomographic Reconstruction Algorithms Utilizing two new algorithms for tomographic reconstruction which incorporate the technique of Equally- Sloped
The Use of Genetic Algorithms in Multilayer Mirror Optimization
Hart, Gus
The Use of Genetic Algorithms in Multilayer Mirror Optimization by Shannon Lunt March 1999 of the Chromosomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 6 Flow chart of the Genetic Algorithm.7 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2 Genetic
Two improved harmony search algorithms for solving engineering optimization problems
NASA Astrophysics Data System (ADS)
Jaberipour, Majid; Khorram, Esmaile
This paper describes two new harmony search (HS) meta-heuristic algorithms for engineering optimization problems with continuous design variables. The key difference between these algorithms and traditional (HS) method is in the way of adjusting bandwidth (bw). bw is very important factor for the high efficiency of the harmony search algorithms and can be potentially useful in adjusting convergence rate of algorithms to optimal solution. First algorithm, proposed harmony search (PHS), introduces a new definition of bandwidth (bw). Second algorithm, improving proposed harmony search (IPHS) employs to enhance accuracy and convergence rate of PHS algorithm. In IPHS, non-uniform mutation operation is introduced which is combination of Yang bandwidth and PHS bandwidth. Various engineering optimization problems, including mathematical function minimization problems and structural engineering optimization problems, are presented to demonstrate the effectiveness and robustness of these algorithms. In all cases, the solutions obtained using IPHS are in agreement or better than those obtained from other methods.
A Distributed Particle Swarm Optimization Algorithm for Swarm Robotic Applications
James M. Hereford
We have derived a version of the particle swarm optimization algorithm that is suitable for a swarm consisting of a large number of small, mobile robots. The algorithm, called the distributed PSO (dPSO), is for \\
Optimal placement of sensors for structural system identification and health monitoring using a hybrid swarm intelligence technique
NASA Astrophysics Data System (ADS)
Rama Mohan Rao, A.; Anandakumar, Ganesh
Setting up a health monitoring system for large-scale civil engineering structures requires a large number of sensors and the placement of these sensors is of great significance for such spatially separated large structures. In this paper, we present an optimal sensor placement (OSP) algorithm by treating OSP as a combinatorial optimization problem which is solved using a swarm intelligence technique called particle swarm optimization (PSO). We propose a new hybrid PSO algorithm by combining a self-configurable PSO with the Nelder-Mead algorithm to solve this rather difficult combinatorial problem of OSP. The proposed algorithm aims precisely to achieve the best identification of modal frequencies and mode shapes. Numerical experiments have been carried out by considering civil engineering structures to evaluate the performance of the proposed swarm-intelligence-based OSP algorithm. Numerical studies indicate that the proposed hybrid PSO algorithm generates sensor configurations superior to the conventional iterative information-based approaches which have been popularly used for large structures. Further, the proposed hybrid PSO algorithm exhibits superior convergence characteristics when compared to other PSO counterparts.
Optimizing two-pass connected-component labeling algorithms
Kesheng Wu; Ekow J. Otoo; Kenji Suzuki
Wepresent two optimization strategiestoimprove connected component labeling algorithms. Taking together, they form an efficient two-pass labeling algorithm that is fast and theoretically optimal. The first optimization strategy re- duces the number of neighboring pixels accessed through the use of a decision tree, and the second one streamlines the union-find algorithms usedto trackequivalentlabels.Weshow that thefirst strategyreducestheaveragenumber of neighbors accessed by a
Optimization of Algorithms for Ion Mobility Calculations
Shvartsburg, Alexandre A.; Mashkevich, Stefan V.; Baker, Erin Shammel; Smith, Richard D.
Ion mobility spectrometry (IMS) is increasingly employed to probe the structures of gas-phase ions, particularly those of proteins and other biological macromolecules. This process involves comparing measured mobilities with those computed for potential geometries, which requires evaluation of orientationally averaged cross sections using some approximate treatment of ion-buffer gas collisions. Two common models are the Projection Approximation (PA) and Exact Hard-Spheres Scattering (EHSS) that represent ions as collections of hard spheres. Though calculations for large ions and/or conformer ensembles take significant time, no algorithmic optimization had been explored. Previous EHSS programs were dominated by ion rotation operations that allow orientational averaging. We have developed two new algorithms for PA and EHSS calculations: one simplifies those operations and greatly reduces their number, and the other disposes of them altogether by propagating trajectories from a random origin. The new algorithms were tested for a representative set of seven ion geometries including diverse sizes and shapes. While the best choice depends on the geometry in a non-obvious way, the difference between the two codes is generally modest. Both are much more efficient than the existing software, for example faster than the widely used Mobcal (implementing EHSS) ~10 - 30 fold.
A Hybrid Graph Representation for Recursive Backtracking Algorithms
NASA Astrophysics Data System (ADS)
Abu-Khzam, Faisal N.; Langston, Michael A.; Mouawad, Amer E.; Nolan, Clinton P.
Many exact algorithms for NP-hard graph problems adopt the old Davis-Putman branch-and-reduce paradigm. The performance of these algorithms often suffers from the increasing number of graph modifications, such as deletions, that reduce the problem instance and have to be "taken back" frequently during the search process. The use of efficient data structures is necessary for fast graph modification modules as well as fast take-back procedures. In this paper, we investigate practical implementation-based aspects of exact algorithms by providing a hybrid graph representation that addresses the take-back challenge and combines the advantage of {O}(1) adjacency-queries in adjacency-matrices with the advantage of efficient neighborhood traversal in adjacency-lists.
Automatic Music Genre Classification Using Hybrid Genetic Algorithms
George V. Karkavitsas; George A. Tsihrintzis
This paper aims at developing an Automatic Music Genre Classification system and focuses on calculating algorithms that (ideally) can predict the music class in which a music file belongs. The proposed system is based on techniques from the fields of Signal Processing, Pattern Recognition, and Information Retrieval, as well as Heuristic Optimization Methods. One thousand music files are used for
Nonlinear inversion of potential-field data using a hybrid-encoding genetic algorithm
Chen, C.; Xia, J.; Liu, J.; Feng, G.
Using a genetic algorithm to solve an inverse problem of complex nonlinear geophysical equations is advantageous because it does not require computer gradients of models or "good" initial models. The multi-point search of a genetic algorithm makes it easier to find the globally optimal solution while avoiding falling into a local extremum. As is the case in other optimization approaches, the search efficiency for a genetic algorithm is vital in finding desired solutions successfully in a multi-dimensional model space. A binary-encoding genetic algorithm is hardly ever used to resolve an optimization problem such as a simple geophysical inversion with only three unknowns. The encoding mechanism, genetic operators, and population size of the genetic algorithm greatly affect search processes in the evolution. It is clear that improved operators and proper population size promote the convergence. Nevertheless, not all genetic operations perform perfectly while searching under either a uniform binary or a decimal encoding system. With the binary encoding mechanism, the crossover scheme may produce more new individuals than with the decimal encoding. On the other hand, the mutation scheme in a decimal encoding system will create new genes larger in scope than those in the binary encoding. This paper discusses approaches of exploiting the search potential of genetic operations in the two encoding systems and presents an approach with a hybrid-encoding mechanism, multi-point crossover, and dynamic population size for geophysical inversion. We present a method that is based on the routine in which the mutation operation is conducted in the decimal code and multi-point crossover operation in the binary code. The mix-encoding algorithm is called the hybrid-encoding genetic algorithm (HEGA). HEGA provides better genes with a higher probability by a mutation operator and improves genetic algorithms in resolving complicated geophysical inverse problems. Another significant result is that final solution is determined by the average model derived from multiple trials instead of one computation due to the randomness in a genetic algorithm procedure. These advantages were demonstrated by synthetic and real-world examples of inversion of potential-field data. ?? 2005 Elsevier Ltd. All rights reserved.
Nonlinear inversion of potential-field data using a hybrid-encoding genetic algorithm
NASA Astrophysics Data System (ADS)
Chen, Chao; Xia, Jianghai; Liu, Jiangping; Feng, Guangding
Using a genetic algorithm to solve an inverse problem of complex nonlinear geophysical equations is advantageous because it does not require computer gradients of models or "good" initial models. The multi-point search of a genetic algorithm makes it easier to find the globally optimal solution while avoiding falling into a local extremum. As is the case in other optimization approaches, the search efficiency for a genetic algorithm is vital in finding desired solutions successfully in a multi-dimensional model space. A binary-encoding genetic algorithm is hardly ever used to resolve an optimization problem such as a simple geophysical inversion with only three unknowns. The encoding mechanism, genetic operators, and population size of the genetic algorithm greatly affect search processes in the evolution. It is clear that improved operators and proper population size promote the convergence. Nevertheless, not all genetic operations perform perfectly while searching under either a uniform binary or a decimal encoding system. With the binary encoding mechanism, the crossover scheme may produce more new individuals than with the decimal encoding. On the other hand, the mutation scheme in a decimal encoding system will create new genes larger in scope than those in the binary encoding. This paper discusses approaches of exploiting the search potential of genetic operations in the two encoding systems and presents an approach with a hybrid-encoding mechanism, multi-point crossover, and dynamic population size for geophysical inversion. We present a method that is based on the routine in which the mutation operation is conducted in the decimal code and multi-point crossover operation in the binary code. The mix-encoding algorithm is called the hybrid-encoding genetic algorithm (HEGA). HEGA provides better genes with a higher probability by a mutation operator and improves genetic algorithms in resolving complicated geophysical inverse problems. Another significant result is that final solution is determined by the average model derived from multiple trials instead of one computation due to the randomness in a genetic algorithm procedure. These advantages were demonstrated by synthetic and real-world examples of inversion of potential-field data.
Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications
Swagatam Das; Arijit Biswas; Sambarta Dasgupta; Ajith Abraham
Bacterial foraging optimization algorithm (BFOA) has been widely accepted as a global optimization algorithm of current interest\\u000a for distributed optimization and control. BFOA is inspired by the social foraging behavior of Escherichia coli. BFOA has already drawn the attention of researchers because of its efficiency in solving real-world optimization problems\\u000a arising in several application domains. The underlying biology behind the
Hybrid inversion algorithm for nonlinear retrieval problems and its use for water vapor profiling based on microwave sounder data
NASA Astrophysics Data System (ADS)
Rieder, Markus J.; Kirchengast, Gottfried
It is shown that geophysical inversion problems can be solved by usage of a hybrid algorithm, which combines optimal estimation with further optimization techniques. We employ a Bayesian approach to nonlinear inversion and discuss several extensions of this method. Especially, a sensible guess of a priori information, the shape of the probability density functions, the utility of Monte Carlo methods, and the advantages of simulated annealing have been investigated. All these techniques furnish capability of retrieving state vectors, which depend on the data in a highly nonlinear manner. A combination of these powerful tools can provide solutions to questions that cannot be tackled with standard inversion methods properly. As a moderately nonlinear optimization problem, profiling of water vapor based on downlooking microwave sounder data is a typical geophysical problem that could not be treated with standard inversion algebra adequately. Based on synthetic state vector data, we show the potential and the characteristics features of all of the hybrid algorithm's components contributing to the retrieval of the state. The hybrid algorithm has been employed in a way that it is able to provide humidity profiles in a numerically stable and computationally efficient manner. Using this example application, the benefit of a hybrid approach is demonstrated.
Optimal path planning in Rapid Prototyping based on genetic algorithm
Yang Weidong
One of important researches in rapid prototyping (RP) is to optimize the path planning which affects the efficiency and building quality of RP system. But it is very difficult to solve its optimization by traditional methods. Genetic algorithms (GAs) are excellent approaches to solving these complex problems in optimization with difficult constraints. The classic path-planning optimization problem has been shown
Adaptation and optimization of basic operations for an unstructured mesh CFD algorithm for computation on massively parallel accelerators
NASA Astrophysics Data System (ADS)
Bogdanov, P. B.; Gorobets, A. V.; Sukov, S. A.
The design of efficient algorithms for large-scale gas dynamics computations with hybrid (heterogeneous) computing systems whose high performance relies on massively parallel accelerators is addressed. A high-order accurate finite volume algorithm with polynomial reconstruction on unstructured hybrid meshes is used to compute compressible gas flows in domains of complex geometry. The basic operations of the algorithm are implemented in detail for massively parallel accelerators, including AMD and NVIDIA graphics processing units (GPUs). Major optimization approaches and a computation transfer technique are covered. The underlying programming tool is the Open Computing Language (OpenCL) standard, which performs on accelerators of various architectures, both existing and emerging.
Solving constrained optimization problems with hybrid particle swarm optimization
Erwie Zahara; Chia-Hsin Hu
Constrained optimization problems (COPs) are very important in that they frequently appear in the real world. A COP, in which both the function and constraints may be nonlinear, consists of the optimization of a function subject to constraints. Constraint handling is one of the major concerns when solving COPs with particle swarm optimization (PSO) combined with the Nelder-Mead simplex search
Hybrid and optical implementation of the Deutsch-Jozsa algorithm
Luis A. Garcia; Jagdish R. Luthra
A hybrid model of the Deutsch-Jozsa algorithm is presented, inspired by the proposals of hybrid computation by S. Lloyd and P. van Loock et. al. The model is based on two observations made about both the discrete and continuous algorithms already available. First, the Fourier transform is a single-step operation in a continuous-variable (CV) setting. Additionally, any implementation of the oracle is nontrivial in both schemes. The steps of the computation are very similar to those in the CV algorithm, with the main difference being the way in which the qunats, or quantum units of analogic information, and the qubits interact in the oracle. Using both discrete and continuous states of light, linear devices, and photo-detection, an optical implementation of the oracle is proposed. For simplicity, infinitely squeezed states are used in the continuous register, whereas the optical qubit is encoded in the dual-rail logic of the KLM protocol. The initial assumption of ideal states as qunats will be dropped to study the effects of finite squeezing in the quality of the computation.
A new adaptive hybrid electromagnetic damper: modelling, optimization, and experiment
NASA Astrophysics Data System (ADS)
Asadi, Ehsan; Ribeiro, Roberto; Behrad Khamesee, Mir; Khajepour, Amir
This paper presents the development of a new electromagnetic hybrid damper which provides regenerative adaptive damping force for various applications. Recently, the introduction of electromagnetic technologies to the damping systems has provided researchers with new opportunities for the realization of adaptive semi-active damping systems with the added benefit of energy recovery. In this research, a hybrid electromagnetic damper is proposed. The hybrid damper is configured to operate with viscous and electromagnetic subsystems. The viscous medium provides a bias and fail-safe damping force while the electromagnetic component adds adaptability and the capacity for regeneration to the hybrid design. The electromagnetic component is modeled and analyzed using analytical (lumped equivalent magnetic circuit) and electromagnetic finite element method (FEM) (COMSOL� software package) approaches. By implementing both modeling approaches, an optimization for the geometric aspects of the electromagnetic subsystem is obtained. Based on the proposed electromagnetic hybrid damping concept and the preliminary optimization solution, a prototype is designed and fabricated. A good agreement is observed between the experimental and FEM results for the magnetic field distribution and electromagnetic damping forces. These results validate the accuracy of the modeling approach and the preliminary optimization solution. An analytical model is also presented for viscous damping force, and is compared with experimental results The results show that the damper is able to produce damping coefficients of 1300 and 0�238 N s m?1 through the viscous and electromagnetic components, respectively.
Optimizing clustering algorithm in mobile ad hoc networks using genetic algorithmic approach
Damla Turgut; Sajal K. Das; Ramez Elmasri; Begumhan Turgut
We show how genetic algorithms can be useful in enhancing the performance of clustering algorithms in mobile ad hoc networks. In particular, we optimize our recently proposed weighted clustering algorithm (WCA). The problem formulation along with the parameters are mapped to individual chromosomes as input to the genetic algorithmic technique. Encoding the individual chromosomes is an essential part of the
Ivan Arsie; Gianfranco Rizzo; Marco Sorrentino
The paper deals with a detailed study on the optimal sizing of a solar hybrid car, based on a longitudinal vehicle dynamic model and considering energy flows, weight and costs. The model describes the effects of solar panels area and position, vehicle dimensions and propulsion system components on vehicle performance, weight, fuel savings and costs. It is shown that significant
Series hybrid vehicles and optimized hydrogen engine design
J. R. Smith; S. Aceves; P. Vanblarigan
Lawrence Livermore, Sandia Livermore and Los Alamos National Laboratories have a joint project to develop an optimized hydrogen fueled engine for series hybrid automobiles. The major divisions of responsibility are: system analysis, engine design and kinetics modeling by LLNL; performance and emission testing, and friction reduction by SNL; computational fluid mechanics and combustion modeling by LANL. This project is a
Hybrid vehicle system studies and optimized hydrogen engine design
J. R. Smith; S. Aceves
We have done system studies of series hydrogen hybrid automobiles that approach the PNGV design goal of 34 km\\/liter (80 mpg), for 384 km (240 mi) and 608 km (380 mi) ranges. Our results indicate that such a vehicle appears feasible using an optimized hydrogen engine. We have evaluated the impact of various on-board storage options on fuel economy. Experiments
JOURNAL OF LATEX CLASS FILES, CDC 2006 1 A Maximum Principle for Hybrid Optimal Control
Maume-Deschamps, Véronique
of the discrete variables. For some simple cases of hybrid optimal control problems, it can be used to carry outJOURNAL OF LATEX CLASS FILES, CDC 2006 1 A Maximum Principle for Hybrid Optimal Control Problems class of hybrid optimal control problems, in which the dynamics of the constituent processes take
Routing Optimization Heuristics Algorithms for Urban Solid Waste Transportation Management
During the last decade, metaheuristics have become increasingly popular for effectively confronting difficult combinatorial optimization problems. In the present paper, two individual meatheuristic algorithmic solutions, the ArcGIS Network Analyst and the Ant Colony System (ACS) algorithm, are introduced, implemented and discussed for the identification of optimal routes in the case of Municipal Solid Waste (MSW) collection. Both proposed applications are
The Routing Optimization Based on Improved Artificial Fish Swarm Algorithm
Xiaojuan Shan; Mingyan Jiang; Jingpeng Li
A novel algorithm named artificial fish swarm algorithm (AFSA) is discussed to solve the problem of routing optimization in computer communication networks. An improved AFSA (IAFSA) with the taboo table and a new parameter is proposed to increase the global optimum capability and the neighborhood search ability of AFSA. A mathematical model of routing optimization based on the minimal time
Optimization of combined cycle power plants using evolutionary algorithms
Christoph Koch; Frank Cziesla; George Tsatsaronis
This paper deals with the application of an evolutionary algorithm to the minimization of the product cost of complex combined cycle power plants. Both the design configuration (process structure) and the process variables are optimized simultaneously. The optimization algorithm can choose among several design options included in a superstructure of the power plant such as different gas turbine systems available
Chaotically encoded particle swarm optimization algorithm and its applications
Bilal Alatas; Erhan Akin
This paper proposes a novel particle swarm optimization (PSO) algorithm, chaotically encoded particle swarm optimization algorithm (CENPSOA), based on the notion of chaos numbers that have been recently proposed for a novel meaning to numbers. In this paper, various chaos arithmetic and evaluation measures that can be used in CENPSOA have been described. Furthermore, CENPSOA has been designed to be
A dynamic inertia weight particle swarm optimization algorithm
Bin Jiao; Zhigang Lian; Xingsheng Gu
Particle swarm optimization (PSO) algorithm has been developing rapidly and has been applied widely since it was introduced, as it is easily understood and realized. This paper presents an improved particle swarm optimization algorithm (IPSO) to improve the performance of standard PSO, which uses the dynamic inertia weight that decreases according to iterative generation increasing. It is tested with a
An improved particle swarm optimization algorithm for unit commitment
B. Zhao; C. X. Guo; B. R. Bai; Y. J. Cao
This paper presents an improved particle swarm optimization algorithm (IPSO) for power system unit commitment. IPSO is an extension of the standard particle swarm optimization algorithm (PSO) which uses more particles� information to control the mutation operation, and is similar to the social society in that a group of leaders could make better decisions. The convergence property of the proposed
An improved marriage in honey bees optimization algorithm for single objective unconstrained optimization.
Celik, Yuksel; Ulker, Erkan
Marriage in honey bees optimization (MBO) is a metaheuristic optimization algorithm developed by inspiration of the mating and fertilization process of honey bees and is a kind of swarm intelligence optimizations. In this study we propose improved marriage in honey bees optimization (IMBO) by adding Levy flight algorithm for queen mating flight and neighboring for worker drone improving. The IMBO algorithm's performance and its success are tested on the well-known six unconstrained test functions and compared with other metaheuristic optimization algorithms. PMID:23935416
An Improved Particle Swarm Optimization Algorithm Mimicking Territorial Dispute Between Groups for Multimodal Function Optimization Problems
Jang-Ho Seo; Chang-Hwan Im; Sang-Yeop Kwak; Cheol-Gyun Lee; Hyun-Kyo Jung
In the present paper, an improved particle swarm optimization (PSO) algorithm for multimodal function optimization is proposed. The new algorithm, named auto-tuning multigrouped PSO (AT-MGPSO) algorithm mimics natural phenomena in ecosystem such as territorial dispute between different group members and immigration of weak groups, resulting in automatic determination of the size of each group's territory and robust convergence. The usefulness
Performance evaluation of TRIBES, an adaptive particle swarm optimization algorithm
Yann Cooren; Maurice Clerc; Patrick Siarry
This paper presents a study of the performance of TRIBES, an adaptive particle swarm optimization algorithm. Particle Swarm\\u000a Optimization (PSO) is a biologically-inspired optimization method. Recently, researchers have used it effectively in solving\\u000a various optimization problems. However, like most optimization heuristics, PSO suffers from the drawback of being greatly\\u000a influenced by the selection of its parameter values. Thus, the common
An improved hybrid encoding cuckoo search algorithm for 0-1 knapsack problems.
Feng, Yanhong; Jia, Ke; He, Yichao
Cuckoo search (CS) is a new robust swarm intelligence method that is based on the brood parasitism of some cuckoo species. In this paper, an improved hybrid encoding cuckoo search algorithm (ICS) with greedy strategy is put forward for solving 0-1 knapsack problems. First of all, for solving binary optimization problem with ICS, based on the idea of individual hybrid encoding, the cuckoo search over a continuous space is transformed into the synchronous evolution search over discrete space. Subsequently, the concept of confidence interval (CI) is introduced; hence, the new position updating is designed and genetic mutation with a small probability is introduced. The former enables the population to move towards the global best solution rapidly in every generation, and the latter can effectively prevent the ICS from trapping into the local optimum. Furthermore, the greedy transform method is used to repair the infeasible solution and optimize the feasible solution. Experiments with a large number of KP instances show the effectiveness of the proposed algorithm and its ability to achieve good quality solutions. PMID:24527026
An Improved Hybrid Encoding Cuckoo Search Algorithm for 0-1 Knapsack Problems
Feng, Yanhong; Jia, Ke; He, Yichao
Cuckoo search (CS) is a new robust swarm intelligence method that is based on the brood parasitism of some cuckoo species. In this paper, an improved hybrid encoding cuckoo search algorithm (ICS) with greedy strategy is put forward for solving 0-1 knapsack problems. First of all, for solving binary optimization problem with ICS, based on the idea of individual hybrid encoding, the cuckoo search over a continuous space is transformed into the synchronous evolution search over discrete space. Subsequently, the concept of confidence interval (CI) is introduced; hence, the new position updating is designed and genetic mutation with a small probability is introduced. The former enables the population to move towards the global best solution rapidly in every generation, and the latter can effectively prevent the ICS from trapping into the local optimum. Furthermore, the greedy transform method is used to repair the infeasible solution and optimize the feasible solution. Experiments with a large number of KP instances show the effectiveness of the proposed algorithm and its ability to achieve good quality solutions. PMID:24527026
Discrete Optimization Algorithms in Real-Time Visual Tracking
Miguel A. Patricio; Iv�n Dot�; Jes�s Garc�a; Antonio Berlanga; Jos� M. Molina
In this work we introduce a novel formulation of the association problem in visual tracking systems as a discrete optimization problem. The full data association problem is formulated as a search for the best tracking configuration to match hypothesis. We have implemented three local search algorithms: Hill Climbing, Simulated Annealing, and Tabu Search algorithms. These algorithms are guided by heuristic
An Optimal Coarse-grained Arc Consistency Algorithm
Paris-Sud XI, Universit� de
An Optimal Coarse-grained Arc Consistency Algorithm Christian Bessiere LIRMM-CNRS (UMR 5506) 161 the propagation in an efficient and effec- tive fashion. There are two classes of propagation algorithms for general constraints: fine-grained algorithms where the removal of a value for a variable
While heuristic optimization is applied in environmental applications, ad-hoc algorithm configuration is typical. We use a multi-layer sorptive barrier design problem as a benchmark for an algorithm-tuning procedure, as applied to three heuristics (genetic algorithms, simulated ...
Distributed Genetic Algorithms with New Sharing Approach Multiobjective Optimization Problems
Coello, Carlos A. Coello
Distributed Genetic Algorithms with New Sharing Approach Multiobjective Optimization email@example.com email@example.com 1 Abstract� this paper, a new distributed genetic algorithm multiobjective and those in the relationship of trade�off. genetic algorithm powerful timization methods based mechanics
Quantum-Behaved Particle Swarm Optimization Algorithm with Controlled Diversity
Jun Sun; Wenbo Xu; Wei Fang
\\u000a Premature convergence, the major problem that confronts evolutionary algorithms, is also encountered with the Particle Swarm\\u000a Optimization (PSO) algorithm. In the previous work [11], [12], [13], the Quantum-behaved Particle Swarm (QPSO) is proposed.\\u000a This novel algorithm is a global-convergence-guaranteed and has a better search ability than the original PSO. But like other\\u000a evolutionary optimization technique, premature in the QPSO is
Solving the vehicle routing problem by a hybrid meta-heuristic algorithm
NASA Astrophysics Data System (ADS)
Yousefikhoshbakht, Majid; Khorram, Esmaile
The vehicle routing problem (VRP) is one of the most important combinational optimization problems that has nowadays received much attention because of its real application in industrial and service problems. The VRP involves routing a fleet of vehicles, each of them visiting a set of nodes such that every node is visited by exactly one vehicle only once. So, the objective is to minimize the total distance traveled by all the vehicles. This paper presents a hybrid two-phase algorithm called sweep algorithm (SW) + ant colony system (ACS) for the classical VRP. At the first stage, the VRP is solved by the SW, and at the second stage, the ACS and 3-opt local search are used for improving the solutions. Extensive computational tests on standard instances from the literature confirm the effectiveness of the presented approach.
A hybrid of bees algorithm and flux balance analysis (BAFBA) for the optimisation of microbial strains.
Choon, Yee Wen; Mohamad, Mohd Saberi; Deris, Safaai; Illias, Rosli Md
The development of microbial production system has become popular in recent years as microbial hosts offer a number of unique advantages for both native and heterologous small-molecules. However, the main drawback is low yield or productivity of the desired products. Optimisation algorithms are implemented in previous works to identify the effects of gene knockout. Nevertheless, the previous works faced performance issue. Thus, a hybrid of Bees Algorithm and Flux Balance Analysis (BAFBA) is proposed in this paper to improve the performance in predicting optimal sets of gene deletion for maximising the growth rate and production yield of certain metabolite. This paper involves two datasets which are E. coli and S. cerevisiae. The list of knockout genes, growth rate and production yield after the deletion are the results from the experiments. BAFBA presents better results compared to the other methods and the identified list may be useful in solving genetic engineering problems. PMID:25796740
A Hybrid Metaheuristic for Biclustering Based on Scatter Search and Genetic Algorithms
NASA Astrophysics Data System (ADS)
Nepomuceno, Juan A.; Troncoso, Alicia; Aguilar�Ruiz, Jes�s S.
In this paper a hybrid metaheuristic for biclustering based on Scatter Search and Genetic Algorithms is presented. A general scheme of Scatter Search has been used to obtain high-quality biclusters, but a way of generating the initial population and a method of combination based on Genetic Algorithms have been chosen. Experimental results from yeast cell cycle and human B-cell lymphoma are reported. Finally, the performance of the proposed hybrid algorithm is compared with a genetic algorithm recently published.
Genetic-Algorithm Tool For Search And Optimization
NASA Technical Reports Server (NTRS)
Wang, Lui; Bayer, Steven
SPLICER computer program used to solve search and optimization problems. Genetic algorithms adaptive search procedures (i.e., problem-solving methods) based loosely on processes of natural selection and Darwinian "survival of fittest." Algorithms apply genetically inspired operators to populations of potential solutions in iterative fashion, creating new populations while searching for optimal or nearly optimal solution to problem at hand. Written in Think C.
Optimizing qubit Hamiltonian parameter estimation algorithms using PSO
Alexandr Sergeevich; Stephen D. Bartlett
We develop qubit Hamiltonian single parameter estimation techniques using a Bayesian approach. The algorithms considered are restricted to projective measurements in a fixed basis, and are derived under the assumption that the qubit measurement is much slower than the characteristic qubit evolution. We optimize a non-adaptive algorithm using particle swarm optimization (PSO) and compare with a previously-developed locally-optimal scheme.
Improvement of Text Dependent Speaker Identification System Using Neuro-Genetic Hybrid Algorithm in Office Environmental Conditions
Islam, Md Rabiul
In this paper, an improved strategy for automated text dependent speaker identification system has been proposed in noisy environment. The identification process incorporates the Neuro- Genetic hybrid algorithm with cepstral based features. To remove the background noise from the source utterance, wiener filter has been used. Different speech pre-processing techniques such as start-end point detection algorithm, pre-emphasis filtering, frame blocking and windowing have been used to process the speech utterances. RCC, MFCC, MFCC, MFCC, LPC and LPCC have been used to extract the features. After feature extraction of the speech, Neuro-Genetic hybrid algorithm has been used in the learning and identification purposes. Features are extracted by using different techniques to optimize the performance of the identification. According to the VALID speech database, the highest speaker identification rate of 100.000 percent for studio environment and 82.33 percent for office environmental conditions have been achieved i...
A Probabilistic Analysis of a Simplified Biogeography-Based Optimization Algorithm
Simon, Dan
A Probabilistic Analysis of a Simplified Biogeography-Based Optimization Algorithm Dan Simon 13, 2009 Abstract Biogeography-based optimization (BBO) is a population-based evolutionary algorithm decreases. Key Words � biogeography-based optimization, evolutionary algorithms, probability, Markov
A Genetic Algorithm for Multiobjective Design Optimization in Aerodynamics and
Coello, Carlos A. Coello
A Genetic Algorithm for Multiobjective Design Optimization in Aerodynamics and Electromagnetics R. The objective functions in the optimization problem measure the aerodynamic feasibil� ity based on the drag been optimized with respect to only one discipline such as aerodynamics or electromagnetics. Although
Optimal management of MicroGrid using Bacterial Foraging Algorithm
R. Noroozian; H. Vahedi
This paper focused on optimal operating strategy and cost optimization scheme for a MicroGrid by using Bacterial Foraging Algorithm. Prior to the optimization of the microgrid itself, the system model components from some real manufactural data are constructed. The proposed cost function takes into consideration the costs of the emissions NOx, SO2, and CO2 as well as the operation and
A Parallel Particle Swarm Optimization Algorithm with Communication Strategies
Jui-fang Chang; Shu-chuan Chu; John F. Roddick; Jeng-shyang Pan
Particle swarm optimization (PSO) is an alternative population-based evolutionary computation technique. It has been shown to be capable of optimizing hard mathematical problems in continuous or binary space. We present here a parallel version of the particle swarm optimization (PPSO) algorithm together with three communication strategies which can be used according to the independence of the data. The first strategy
Identifying Optimal Inorganic Nanomateirals for Hybrid Solar Cells
Xiang, H.; Wei, S. H.; Gong, X. G.
As a newly developed photovoltaic technology, organic-inorganic hybrid solar cells have attracted great interest because of the combined advantages from both components. An ideal inorganic acceptor should have a band gap of about 1.5 eV and energy levels of frontier orbitals matching those of the organic polymer in hybrid solar cells. Hybrid density functional calculations are performed to search for optimal inorganic nanomaterials for hybrid solar sells based on poly(3-hexylthiophene) (P3HT). Our results demonstrate that InSb quantum dots or quantum wires can have a band gap of about 1.5 eV and highest occupied molecular orbital level about 0.4 eV lower than P3HT, indicating that they are good candidates for use in hybrid solar cells. In addition, we predict that chalcopyrite MgSnSb{sub 2} quantum wire could be a low-cost material for realizing high-efficiency hybrid solar cells.
A hybrid multi-objective particle swarm algorithm for a mixed-model assembly line sequencing problem
NASA Astrophysics Data System (ADS)
Rahimi-Vahed, A. R.; Mirghorbani, S. M.; Rabbani, M.
Mixed-model assembly line sequencing is one of the most important strategic problems in the field of production management where diversified customers' demands exist. In this article, three major goals are considered: (i) total utility work, (ii) total production rate variation and (iii) total setup cost. Due to the complexity of the problem, a hybrid multi-objective algorithm based on particle swarm optimization (PSO) and tabu search (TS) is devised to obtain the locally Pareto-optimal frontier where simultaneous minimization of the above-mentioned objectives is desired. In order to validate the performance of the proposed algorithm in terms of solution quality and diversity level, the algorithm is applied to various test problems and its reliability, based on different comparison metrics, is compared with three prominent multi-objective genetic algorithms, PS-NC GA, NSGA-II and SPEA-II. The computational results show that the proposed hybrid algorithm significantly outperforms existing genetic algorithms in large-sized problems.
Solving constrained optimization problems with hybrid particle swarm optimization
NASA Astrophysics Data System (ADS)
Zahara, Erwie; Hu, Chia-Hsin
Constrained optimization problems (COPs) are very important in that they frequently appear in the real world. A COP, in which both the function and constraints may be nonlinear, consists of the optimization of a function subject to constraints. Constraint handling is one of the major concerns when solving COPs with particle swarm optimization (PSO) combined with the Nelder-Mead simplex search method (NM-PSO). This article proposes embedded constraint handling methods, which include the gradient repair method and constraint fitness priority-based ranking method, as a special operator in NM-PSO for dealing with constraints. Experiments using 13 benchmark problems are explained and the NM-PSO results are compared with the best known solutions reported in the literature. Comparison with three different meta-heuristics demonstrates that NM-PSO with the embedded constraint operator is extremely effective and efficient at locating optimal solutions.
Low-thrust orbit transfer optimization with refined Q-law and multi-objective genetic algorithm
NASA Technical Reports Server (NTRS)
Lee, Seungwon; Petropoulos, Anastassios E.; von Allmen, Paul
An optimization method for low-thrust orbit transfers around a central body is developed using the Q-law and a multi-objective genetic algorithm. in the hybrid method, the Q-law generates candidate orbit transfers, and the multi-objective genetic algorithm optimizes the Q-law control parameters in order to simultaneously minimize both the consumed propellant mass and flight time of the orbit tranfer. This paper addresses the problem of finding optimal orbit transfers for low-thrust spacecraft.
A hybrid search algorithm for swarm robots searching in an unknown environment.
Li, Shoutao; Li, Lina; Lee, Gordon; Zhang, Hao
This paper proposes a novel method to improve the efficiency of a swarm of robots searching in an unknown environment. The approach focuses on the process of feeding and individual coordination characteristics inspired by the foraging behavior in nature. A predatory strategy was used for searching; hence, this hybrid approach integrated a random search technique with a dynamic particle swarm optimization (DPSO) search algorithm. If a search robot could not find any target information, it used a random search algorithm for a global search. If the robot found any target information in a region, the DPSO search algorithm was used for a local search. This particle swarm optimization search algorithm is dynamic as all the parameters in the algorithm are refreshed synchronously through a communication mechanism until the robots find the target position, after which, the robots fall back to a random searching mode. Thus, in this searching strategy, the robots alternated between two searching algorithms until the whole area was covered. During the searching process, the robots used a local communication mechanism to share map information and DPSO parameters to reduce the communication burden and overcome hardware limitations. If the search area is very large, search efficiency may be greatly reduced if only one robot searches an entire region given the limited resources available and time constraints. In this research we divided the entire search area into several subregions, selected a target utility function to determine which subregion should be initially searched and thereby reduced the residence time of the target to improve search efficiency. PMID:25386855
An implementation of a parallel ray tracing algorithm on hybrid parallel architecture
Chang-Geun Kwon; Hyo-Kyung Sung; Heung-Moon Choi
We present a parallel ray tracing algorithm on hybrid parallel architecture with processor a farm model to speed up the ray tracing. The hybrid parallel architecture, a hybrid of a tightly- and a loosely-coupled one, is used in which reconfiguration for local and virtual shared memory is made through a crossbar network with a local and global bus. The proposed
Evaluation of a Particle Swarm Algorithm For Biomechanical Optimization
Schutte, Jaco F.; Koh, Byung; Reinbolt, Jeffrey A.; Haftka, Raphael T.; George, Alan D.; Fregly, Benjamin J.
Optimization is frequently employed in biomechanics research to solve system identification problems, predict human movement, or estimate muscle or other internal forces that cannot be measured directly. Unfortunately, biomechanical optimization problems often possess multiple local minima, making it difficult to find the best solution. Furthermore, convergence in gradient-based algorithms can be affected by scaling to account for design variables with different length scales or units. In this study we evaluate a recently-developed version of the particle swarm optimization (PSO) algorithm to address these problems. The algorithm�s global search capabilities were investigated using a suite of difficult analytical test problems, while its scale-independent nature was proven mathematically and verified using a biomechanical test problem. For comparison, all test problems were also solved with three off-the-shelf optimization algorithms�a global genetic algorithm (GA) and multistart gradient-based sequential quadratic programming (SQP) and quasi-Newton (BFGS) algorithms. For the analytical test problems, only the PSO algorithm was successful on the majority of the problems. When compared to previously published results for the same problems, PSO was more robust than a global simulated annealing algorithm but less robust than a different, more complex genetic algorithm. For the biomechanical test problem, only the PSO algorithm was insensitive to design variable scaling, with the GA algorithm being mildly sensitive and the SQP and BFGS algorithms being highly sensitive. The proposed PSO algorithm provides a new off-the-shelf global optimization option for difficult biomechanical problems, especially those utilizing design variables with different length scales or units. PMID:16060353
Optimal and nearly optimal algorithms for approximating polynomial zeros
V. Y. Pan
We substantially improve the known algorithms for approximating all the complex zeros of an nth degree polynomial p(x). Our new algorithms save both Boolean and arithmetic sequential time, versus the previous best algorithms of Sch�nhage [1], Pan [2], and Neff and Reif [3]. In parallel (NC) implementation, we dramatically decrease the number of processors, versus the parallel algorithm of Neff
Nonlinear hybrid Boltzmannparticle-in-cell acceleration algorithm K. L. Cartwright,a)
Wurtele, Jonathan
Nonlinear hybrid Boltzmann�particle-in-cell acceleration algorithm K. L. Cartwright,a) J. P scales. Hybrid electrostatic particle-in-cell PIC algorithms are presented in which most of the electrons distribution. Collisions for PIC electrons are included via a Monte Carlo model, while for the MB electrons
Krill herd: A new bio-inspired optimization algorithm
NASA Astrophysics Data System (ADS)
Gandomi, Amir Hossein; Alavi, Amir Hossein
In this paper, a novel biologically-inspired algorithm, namely krill herd (KH) is proposed for solving optimization tasks. The KH algorithm is based on the simulation of the herding behavior of krill individuals. The minimum distances of each individual krill from food and from highest density of the herd are considered as the objective function for the krill movement. The time-dependent position of the krill individuals is formulated by three main factors: (i) movement induced by the presence of other individuals (ii) foraging activity, and (iii) random diffusion. For more precise modeling of the krill behavior, two adaptive genetic operators are added to the algorithm. The proposed method is verified using several benchmark problems commonly used in the area of optimization. Further, the KH algorithm is compared with eight well-known methods in the literature. The KH algorithm is capable of efficiently solving a wide range of benchmark optimization problems and outperforms the exciting algorithms.
PID controller tuning using multi-objective optimization based on fused Genetic-Immune algorithm and Immune feedback mechanism
Maryam Khoie; Ali Khaki Sedigh; Karim Salahshoor
In this paper, a Genetic-AIS (Artificial Immune System) algorithm is introduced for PID (Proportional- Integral-Derivative) controller tuning using a multi-objective optimization framework. This hybrid Genetic-AIS technique is faster and accurate compared to each individual Genetic or AIS approach. The auto-tuned PID algorithm is then fused in an Immune feedback law based on a nonlinear proportional gain to realize a new
Particle Swarm Optimization Algorithm Based on the Idea of Simulated Annealing
DONG Chaojun; QIU Zulian
Summary Particle swarm optimization (PSO) algorithm is a new population intelligence algorithm and has good performance on optimization. After the standard PSO algorithm and the idea of simulated annealing algorithm had been analyzed, the acceptance of Metropolis rule by probability in the simulated annealing algorithm was introduced in the algorithm of PSO. The simulated annealing-particle swarm optimization was presented. Simulation
An Adaptive Unified Differential Evolution Algorithm for Global Optimization
Qiang, Ji; Mitchell, Chad
In this paper, we propose a new adaptive unified differential evolution algorithm for single-objective global optimization. Instead of the multiple mutation strate- gies proposed in conventional differential evolution algorithms, this algorithm employs a single equation unifying multiple strategies into one expression. It has the virtue of mathematical simplicity and also provides users the flexibility for broader exploration of the space of mutation operators. By making all control parameters in the proposed algorithm self-adaptively evolve during the process of optimization, it frees the application users from the burden of choosing appro- priate control parameters and also improves the performance of the algorithm. In numerical tests using thirteen basic unimodal and multimodal functions, the proposed adaptive unified algorithm shows promising performance in compari- son to several conventional differential evolution algorithms.
Aerodynamic Shape Design of Transonic Airfoils Using Hybrid Optimization Techniques and CFD
Xing, X.Q.
This paper will analyze the effects of using hybrid optimization methods for optimizing objective functions that are determined by computational fluid dynamics solvers for compressible viscous flow for optimal design of ...
The Coral Reefs Optimization Algorithm: A Novel Metaheuristic for Efficiently Solving Optimization Problems
Salcedo-Sanz, S.; Del Ser, J.; Landa-Torres, I.; Gil-L�pez, S.; Portilla-Figueras, J. A.
This paper presents a novel bioinspired algorithm to tackle complex optimization problems: the coral reefs optimization (CRO) algorithm. The CRO algorithm artificially simulates a coral reef, where different corals (namely, solutions to the optimization problem considered) grow and reproduce in coral colonies, fighting by choking out other corals for space in the reef. This fight for space, along with the specific characteristics of the corals' reproduction, produces a robust metaheuristic algorithm shown to be powerful for solving hard optimization problems. In this research the CRO algorithm is tested in several continuous and discrete benchmark problems, as well as in practical application scenarios (i.e., optimum mobile network deployment and off-shore wind farm design). The obtained results confirm the excellent performance of the proposed algorithm and open line of research for further application of the algorithm to real-world problems. PMID:25147860
The coral reefs optimization algorithm: a novel metaheuristic for efficiently solving optimization problems.
Salcedo-Sanz, S; Del Ser, J; Landa-Torres, I; Gil-L�pez, S; Portilla-Figueras, J A
This paper presents a novel bioinspired algorithm to tackle complex optimization problems: the coral reefs optimization (CRO) algorithm. The CRO algorithm artificially simulates a coral reef, where different corals (namely, solutions to the optimization problem considered) grow and reproduce in coral colonies, fighting by choking out other corals for space in the reef. This fight for space, along with the specific characteristics of the corals' reproduction, produces a robust metaheuristic algorithm shown to be powerful for solving hard optimization problems. In this research the CRO algorithm is tested in several continuous and discrete benchmark problems, as well as in practical application scenarios (i.e., optimum mobile network deployment and off-shore wind farm design). The obtained results confirm the excellent performance of the proposed algorithm and open line of research for further application of the algorithm to real-world problems. PMID:25147860
Improved PSO algorithms for electromagnetic optimization
L. Matekovits; M. Mussetta; P. Pirinoli; S. Selleri; R. E. Zich
Some variations over the basic particle swarm algorithm are here proposed, aimed at a more efficient search over the solution space and exhibiting a negligible overhead in complexity and speed. The proposed algorithms are then applied to the test case of a microwave filter to show their superior capabilities with respect to the conventional algorithm.
Optimization and Comparison of Heuristic Control Strategies for Parallel Hybrid-Electric Vehicles
Paderborn, Universit�t
Optimization and Comparison of Heuristic Control Strategies for Parallel Hybrid-Electric Vehicles independent. Thus, these control strategies are predestinated for the use in a real vehicle. Keywords: Hybrid-electric vehicle (HEV), control strategies, optimization. 1. Introduction Due to the structure of hybrid-electric
A novel optimization sizing model for hybrid solar-wind power generation system
Hongxing Yang; Lin Lu; Wei Zhou
This paper develops the Hybrid Solar-Wind System Optimization Sizing (HSWSO) model, to optimize the capacity sizes of different components of hybrid solar-wind power generation systems employing a battery bank. The HSWSO model consists of three parts: the model of the hybrid system, the model of Loss of Power Supply Probability (LPSP) and the model of the Levelised Cost of Energy
Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem
NASA Astrophysics Data System (ADS)
Chen, Wei
In this paper, we discuss the portfolio optimization problem with real-world constraints under the assumption that the returns of risky assets are fuzzy numbers. A new possibilistic mean-semiabsolute deviation model is proposed, in which transaction costs, cardinality and quantity constraints are considered. Due to such constraints the proposed model becomes a mixed integer nonlinear programming problem and traditional optimization methods fail to find the optimal solution efficiently. Thus, a modified artificial bee colony (MABC) algorithm is developed to solve the corresponding optimization problem. Finally, a numerical example is given to illustrate the effectiveness of the proposed model and the corresponding algorithm.
A study of speech emotion recognition based on hybrid algorithm
NASA Astrophysics Data System (ADS)
Zhu, Ju-xia; Zhang, Chao; Lv, Zhao; Rao, Yao-quan; Wu, Xiao-pei
To effectively improve the recognition accuracy of the speech emotion recognition system, a hybrid algorithm which combines Continuous Hidden Markov Model (CHMM), All-Class-in-One Neural Network (ACON) and Support Vector Machine (SVM) is proposed. In SVM and ACON methods, some global statistics are used as emotional features, while in CHMM method, instantaneous features are employed. The recognition rate by the proposed method is 92.25%, with the rejection rate to be 0.78%. Furthermore, it obtains the relative increasing of 8.53%, 4.69% and 0.78% compared with ACON, CHMM and SVM methods respectively. The experiment result confirms the efficiency of distinguishing anger, happiness, neutral and sadness emotional states.
Algorithm 896: LSA: Algorithms for large-scale optimization
Ladislav Luksan; Ctirad Matonoha; Jan Vlcek
We present 14 basic Fortran subroutines for large-scale unconstrained and box constrained optimization and large-scale systems of nonlinear equations. Subroutines PLIS and PLIP, intended for dense general optimization problems, are based on limited-memory variable metric methods. Subroutine PNET, also intended for dense general optimization problems, is based on an inexact truncated Newton method. Subroutines PNED and PNEC, intended for sparse
Application of a gradient-based algorithm to structural optimization
Ghisbain, Pierre
Optimization methods have shown to be efficient at improving structural design, but their use is limited in the engineering practice by the difficulty of adapting state-of-the-art algorithms to particular engineering ...
Provably Good Approximation Algorithms for Optimal Kinodynamic Planning: Robots with
Richardson, David
Provably Good Approximation Algorithms for Optimal Kinodynamic Planning: Robots with Decoupled-7501 Patrick Xavier Sandia National Laboratories, Albuquerque NM 87185-0951 Keywords: robot motion planning, kinodynamics, polyhedral obstacles Abstract: We consider the following problem: given a robot system, nd
Optimal scaling of the ADMM algorithm for distributed quadratic ...
Dec 11, 2014 ... algorithm for a class of distributed quadratic programming problems. ... Numerical simulations justify our results and highlight the benefits of optimally .... the past iterates when computing the next. ...... E. Chu, B. Peleato, and J. Eckstein, �
Genetic Algorithms applications to optimization and system identification
Lin, Yun-Jeng
Genetic Algorithms (GA) are very different from the traditional optimization techniques. GA is a new generation of artificial intelligence and its principles mimic the behavior of the biologic genes in the natural world. Its execution is simple...
A hybrid boundary condition for robust particle swarm optimization
Tony Huang; Ananda Sanagavarapu Mohan
The particle swarm optimization (PSO) technique is a powerful stochastic evolutionary algorithm that can be used to find the global optimum solution in a complex search space. However, it has been observed that there is a great variation in its performance due to the dimensionality of the problem and the location of the global optimum with respect to the boundaries
Hybrid Model for Building Performance Diagnosis and Optimal Control
Wang, S.; Xu, X.
of three resistances and two capacitances. The resistances and capacitances of the 2R2C model are assumed to be constant. A GA (genetic algorithm)-based method is developed for model parameter identification by searching the optimal parameters of 3R2C...
Parallel projected variable metric algorithms for unconstrained optimization
NASA Technical Reports Server (NTRS)
Freeman, T. L.
The parallel variable metric optimization algorithms of Straeter (1973) and van Laarhoven (1985) are reviewed, and the possible drawbacks of the algorithms are noted. By including Davidon (1975) projections in the variable metric updating, researchers can generalize Straeter's algorithm to a family of parallel projected variable metric algorithms which do not suffer the above drawbacks and which retain quadratic termination. Finally researchers consider the numerical performance of one member of the family on several standard example problems and illustrate how the choice of the displacement vectors affects the performance of the algorithm.
A Unified Differential Evolution Algorithm for Global Optimization
Qiang, Ji; Mitchell, Chad
Abstract?In this paper, we propose a new unified differential evolution (uDE) algorithm for single objective global optimization. Instead of selecting among multiple mutation strategies as in the conventional differential evolution algorithm, this algorithm employs a single equation as the mutation strategy. It has the virtue of mathematical simplicity and also provides users the flexbility for broader exploration of different mutation strategies. Numerical tests using twelve basic unimodal and multimodal functions show promising performance of the proposed algorithm in comparison to convential differential evolution algorithms.
Optimization of image processing algorithms on mobile platforms
Pramod Poudel; Mukul Shirvaikar
This work presents a technique to optimize popular image processing algorithms on mobile platforms such as cell phones, net-books and personal digital assistants (PDAs). The increasing demand for video applications like context-aware computing on mobile embedded systems requires the use of computationally intensive image processing algorithms. The system engineer has a mandate to optimize them so as to meet real-time
Increasing EtherCAT performance using frame size optimization algorithm
Mladen Knezic; Branko Dokic; Zeljko Ivanovic
EtherCAT protocol is a popular Real-Time Ethernet solution that offers the highest communication efficiency in a number of operating conditions. However, for huge networks with several hundreds of devices distributed in the field, EtherCAT performance can become a critical factor. In this paper, we propose a solution for increasing EtherCAT performance using a frame size optimization algorithm. The optimization algorithm
Standard Harmony Search Algorithm for Structural Design Optimization
Kang Seok Lee
Most engineering optimization algorithms are based on numerical linear and nonlinear programming methods that require substantial\\u000a gradient information and usually seek to improve the solution in the neighborhood of a starting point. These algorithms, however,\\u000a reveal a limited approach to complicated real-world optimization problems. If there is more than one local optimum in the\\u000a problem, the result may depend on
An Improved Particle Swarm Optimization Algorithm with Disturbance Term
Qingyuan He; Chuanjiu Han
\\u000a The standard particle swarm optimization (PSO) algorithm, existing improvements and their influence to the performance of\\u000a standard PSO are introduced. The framework of PSO basic formula is analyzed. Implied by its three-term structure, the inherent\\u000a shortcoming that trends to local optima is indicated. Then a modified velocity updating formula of particle swarm optimization\\u000a algorithm is declared. The addition of the
Reliability-Based Optimization Using Evolutionary Algorithms
Deb, Kalyanmoy
Uncertainties in design variables and problem parameters are often inevitable and must be considered in an optimization task if reliable optimal solutions are sought. Besides a number of sampling techniques, there exist ...
Genetic algorithm based tomographic flow visualization
Lyons, Donald Paul
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 III EVOLUTIONARY ALGORITHMS FOR TOMOGRAPHIC FLOW VISUALIZATION . . . 18 I. II. III. IV. Genetic Algorithms . General Hybridization Schemes Concurrent Downhill Simplex Method. Hybrid Simplex Genetic Algorithms . . . . . . 1 8.... In section IV an alternative optimization technique is introduced for the purposes of developing a hybrid algorithm. Finally, section V discusses the details of the hybrid between the concurrent downhill simplex method and the Genetic Algorithm. I. Genetic...
The Vector Model of Artificial Physics Optimization Algorithm for Global Optimization Problems
Liping Xie; Jianchao Zeng; Zhuihua Cui
To solve complex global optimization problems, Artificial Physics Optimization (APO) algorithm is presented based on Physicomimetics framework, which is a population-based stochastic algorithm inspired by physical force. The solutions (particles) sampled from the feasible region of the problems are treated as physical individuals. Each individual has a mass, position and velocity. The mass of each individual corresponds to a user-defined
Applying new optimization algorithms to more predictive control
Wright, S.J.
The connections between optimization and control theory have been explored by many researchers and optimization algorithms have been applied with success to optimal control. The rapid pace of developments in model predictive control has given rise to a host of new problems to which optimization has yet to be applied. Concurrently, developments in optimization, and especially in interior-point methods, have produced a new set of algorithms that may be especially helpful in this context. In this paper, we reexamine the relatively simple problem of control of linear processes subject to quadratic objectives and general linear constraints. We show how new algorithms for quadratic programming can be applied efficiently to this problem. The approach extends to several more general problems in straightforward ways.
Double Shock Control Bump design optimization using hybridised evolutionary algorithms
DongSeop Lee; Jacques P�riaux; Jordi Pons-Prats; Gabriel Bugeda; Eugenio Onate
The paper investigates two advanced optimisation methods for solving active flow control device shape design problem and also compares their optimisation efficiency in terms of computational cost and design quality. The first optimisation method uses Hierarchical Asynchronous Parallel Multi-Objective Evolutionary Algorithm (HAPMOEA) and the second uses Hybridized EA with Nash-Game strategies. Both optimisation method are based on a canonical evolution
Optimization of a CNG series hybrid concept vehicle
Aceves, S.M.; Smith, J.R.; Perkins, L.J.; Haney, S.W.; Flowers, D.L.
Compressed Natural Gas (CNG) has favorable characteristics as a vehicular fuel, in terms of fuel economy as well as emissions. Using CNG as a fuel in a series hybrid vehicle has the potential of resulting in very high fuel economy (between 26 and 30 km/liter, 60 to 70 mpg) and very low emissions (substantially lower than Federal Tier II or CARB ULEV). This paper uses a vehicle evaluation code and an optimizer to find a set of vehicle parameters that result in optimum vehicle fuel economy. The vehicle evaluation code used in this analysis estimates vehicle power performance, including engine efficiency and power, generator efficiency, energy storage device efficiency and state-of-charge, and motor and transmission efficiencies. Eight vehicle parameters are selected as free variables for the optimization. The optimum vehicle must also meet two perfect requirements: accelerate to 97 km/h in less than 10 s, and climb an infinitely long hill with a 6% slope at 97 km/h with a 272 kg (600 lb.) payload. The optimizer used in this work was originally developed in the magnetic fusion energy program, and has been used to optimize complex systems, such as magnetic and inertial fusion devices, neutron sources, and mil guns. The optimizer consists of two parts: an optimization package for minimizing non-linear functions of many variables subject to several non-linear equality and/or inequality constraints and a programmable shell that allows interactive configuration and execution of the optimizer. The results of the analysis indicate that the CNG series hybrid vehicle has a high efficiency and low emissions. These results emphasize the advantages of CNG as a near-term alternative fuel for vehicles.
Controlling Convergence of Space-Mapping Algorithms for Engineering Optimization
Slawomir Koziel; John W. Bandler
The problem of convergence properties of space mapping optimization algorithms is addressed. A new weighting scheme in the parameter extraction procedure is introduced that allows us to control the behavior of the space mapping algorithm and force it to converge after a reasonable number of fine model evaluations. An application example is provided.
Optimal reactive power dispatch using an adaptive genetic algorithm
Q. H. Wu; Y. J. Cao; J. Y. Wen
This paper presents an adaptive genetic algorithm (AGA) for optimal reactive power dispatch and voltage control of power systems. In the adaptive genetic algorithm, the probabilities of crossover and mutation, pc and pm, are varied depending on the fitness values of the solutions and the normalized fitness distances between the solutions in the evolution process to prevent premature convergence and
Dynamic Particle Swarm Optimization Algorithm for Resolution of Overlapping Chromatograms
Yufeng Li
Dynamic particle swarm optimization algorithm is proposed in this paper to resolve overlapping chromatographic peaks. To accelerate the convergence speed, clustering degree and evolution velocity are considered simultaneously to adjust inertia weight adaptively. The algorithm is tested on both simulated overlapping chromatographic peaks which are based on exponential modified Gaussian convolution model and experimental overlapping chromatographic peaks of multi-component which
MultiObjective Optimization by Genetic Algorithms : A Review
Coello, Carlos A. Coello
Multi�Objective Optimization by Genetic Algorithms : A Review Hisashi Tamaki Department of Electrical and Electronics Engineering, Kobe University, Rokkodai, Nada�ku, Kobe 657, Japan. tamaki, Japan. firstname.lastname@example.com Abstract--- This paper reviews several genetic algorithm (GA
A Bee Colony Optimization Algorithm for Traveling Salesman Problem
Li-pei Wong; Malcolm Yoke-hean Low; Chin Soon Chong
A bee colony optimization (BCO) algorithm for traveling salesman problem (TSP) is presented in this paper. The BCO model is constructed algorithmically based on the collective intelligence shown in bee foraging behaviour. Experimental results comparing the proposed BCO model with some existing approaches on a set of benchmark problems are presented.
Genetic algorithm optimization applied to electromagnetics: a review
Daniel S. Weile; Eric Michielssen
Genetic algorithms are on the rise in electromagnetics as design tools and problem solvers because of their versatility and ability to optimize in complex multimodal search spaces. This paper describes the basic genetic algorithm and recounts its history in the electromagnetics literature. Also, the application of advanced genetic operators to the field of electromagnetics is described, and design results are
Serial and Parallel Genetic Algorithms as Function Optimizers
V. Scott Gordon; L. Darrell Whitley
Parallel genetic algorithms are often very differentfrom the "traditional" genetic algorithmproposed by Holland, especially withregards to population structure and selectionmechanisms. In this paper we compare severalparallel genetic algorithms across a widerange of optimization functions in an attemptto determine whether these changes have positiveor negative impact on their problemsolvingcapabilities. The findings indicatethat the parallel structures perform as well asor ...
Multiobjective Optimization by Nessy Algorithm Mario Kppen1
Coello, Carlos A. Coello
different from Neuro-GA approaches are its redefined genetic operators. To ensure the neuron based implementation of the Nessy algorithm uses only one neuron in the output layer. This is due to the fact. By using more than one output neuron, the Nessy algorithm is well-suited to multi-objective optimization
Frankenstein's PSO: A Composite Particle Swarm Optimization Algorithm
Marco Antonio Montes de Oca; Thomas St�tzle; Mauro Birattari; Marco Dorigo
During the last decade, many variants of the original particle swarm optimization (PSO) algorithm have been proposed. In many cases, the difference between two variants can be seen as an algorithmic component being present in one variant but not in the other. In the first part of the paper, we present the results and insights obtained from a detailed empirical
Wavelet Threshold Optimization with Artificial Fish Swarm Algorithm
Mingyan Jiang; Dongfeng Yuan
The artificial fish swarm algorithm (AFSA) is discussed in this paper, and an improved optimal wavelet threshold algorithm is presented based on AFSA in signal denoising processing. Simulations show that our method has better performance than the conventional wavelet-based denoising threshold method
Augmented Lagrangian Algorithm for Optimizing Analog Circuit Design
Eindhoven, Technische Universiteit
derivatives of the design metrics and numerical noise is in- herently present (for instance due to adaptive to approximate derivatives. One of the two optimization algorithms available in Adapt is the Nelder{Mead (NM NM. The Nelder{Mead algorithm is very robust but has rather poor performance characteristics
Simplex Optimization Localization Algorithm for Wireless Sensor Networks
Shaoping Zhang; Guohui Li
Accurate, distributed localization algorithms are needed for large scale dense wireless sensor network applications. The Nelder-Mead Simplex Optimization Method (SOM) is applied to solve nondifferentiable problems and can often handle high discontinuity, particularly if it does not occur near the solution. This article proposes a distributed iterative multilateral algorithm. It formulates a function that presents the sum of range errors
An improved particle swarm optimization algorithm for flowshop scheduling problem
Changsheng Zhang; Jigui Sun; Xingjun Zhu; Qingyun Yang
The flowshop scheduling problem has been widely studied and many techniques have been applied to it, but few algorithms based on particle swarm optimization (PSO) have been proposed to solve it. In this paper, an improved PSO algorithm (IPSO) based on the �alldifferent� constraint is proposed to solve the flow shop scheduling problem with the objective of minimizing makespan. It
Particle Swarm Optimization Algorithm in Signal Detection and Blind Extractio
Ying Zhao; Junli Zheng
The particle swarm optimization (PSO) algorithm, which originated as a simulation of a simplified social system, is an evolutionary computation technique. In this paper the binary and real-valued versions of PSO algorithm are exploited in two important signal processing paradigm: multiuser detection (MUD) and blind extraction of sources (BES), respectively. The novel approaches are effective and efficient with parallel processing
A simulated annealing algorithm for constrained Multi-Objective Optimization
Hemant Kumar Singh; Amitay Isaacs; Tapabrata Ray; Warren Smith
In this paper, we introduce a simulated annealing algorithm for constrained Multi-Objective Optimization (MOO). When searching in the feasible region, the algorithm behaves like recently proposed Archived Multi-Objective Simulated Annealing (AMOSA) algorithm [1], whereas when operating in the infeasible region, it tries to minimize constraint violation by moving along Approximate Descent Direction (ADD) [2]. An Archive of non-dominated solutions found
Optimizing performance of hybrid FSO/RF networks in realistic dynamic scenarios
NASA Astrophysics Data System (ADS)
Llorca, Jaime; Desai, Aniket; Baskaran, Eswaran; Milner, Stuart; Davis, Christopher
Hybrid Free Space Optical (FSO) and Radio Frequency (RF) networks promise highly available wireless broadband connectivity and quality of service (QoS), particularly suitable for emerging network applications involving extremely high data rate transmissions such as high quality video-on-demand and real-time surveillance. FSO links are prone to atmospheric obscuration (fog, clouds, snow, etc) and are difficult to align over long distances due the use of narrow laser beams and the effect of atmospheric turbulence. These problems can be mitigated by using adjunct directional RF links, which provide backup connectivity. In this paper, methodologies for modeling and simulation of hybrid FSO/RF networks are described. Individual link propagation models are derived using scattering theory, as well as experimental measurements. MATLAB is used to generate realistic atmospheric obscuration scenarios, including moving cloud layers at different altitudes. These scenarios are then imported into a network simulator (OPNET) to emulate mobile hybrid FSO/RF networks. This framework allows accurate analysis of the effects of node mobility, atmospheric obscuration and traffic demands on network performance, and precise evaluation of topology reconfiguration algorithms as they react to dynamic changes in the network. Results show how topology reconfiguration algorithms, together with enhancements to TCP/IP protocols which reduce the network response time, enable the network to rapidly detect and act upon link state changes in highly dynamic environments, ensuring optimized network performance and availability.
A comparison of optimal and sub-optimal MAP decoding algorithms operating in the log domain
P. Robertson; E. Villebrun; P. Hoeher
For estimating the states or outputs of a Markov process, the symbol-by-symbol MAP algorithm is optimal. However, this algorithm, even in its recursive form, poses technical difficulties because of numerical representation problems, the necessity of nonlinear functions and a high number of additions and multiplications. MAP like algorithms operating in the logarithmic domain presented in the past solve the numerical
What About Wednesday? Approximation Algorithms for Multistage Stochastic Optimization
Anupam Gupta; Martin P�l; Ramamoorthi Ravi; Amitabh Sinha
The field of stochastic optimization studies decision making under uncertainty, when only probabilistic information about the future is available. Finding approximate solutions to well-studied optimization problems (such as Steiner tree, Vertex Cover, and Facility Location, to name but a few) presents new challenges when investigated in this frame- work, which has promoted much research in approximation algorithms. There has been
Algorithms for the Electrical Optimization of Digital MOS Circuits
North Carolina at Chapel Hill, University of
to the transistor sizes in the circuit; no changes in the circuit struc- ture, number of gates or clocking are introduced. Linear algorithms are presented for computing optimal transistor sizes to minimize delay, area through the circuit and computes the optimal transistor sizes to achieve the performance objectives
Hedging Uncertainty: Approximation Algorithms for Stochastic Optimization Problems
R. Ravi; Amitabh Sinha
We study the design of approximation algorithms for stoch- astic combinatorial optimization problems. We formulate the problems in the framework of two-stage stochastic optimization, and provide nearly tight approximations. Our problems range from the simple (shortest path, vertex cover, bin packing) to complex (facility location, set cover), and contain representatives with different approximation ratios. The approximation ratio of the stochastic
Using modifications to Grover's Search algorithm for quantum global optimization
Yipeng Liu; Gary J. Koehler
We study the problem of finding a global optimal solution to discrete optimization problems using a heuristic based on quantum computing methods. (Knowledge of quantum computing ideas is not necessary to read this paper.) We focus on a successful quantum computing method introduced by Baritompa, Bulger, and Wood, that we refer to as the BBW algorithm, and develop two modifications.
Model Specification Searches Using Ant Colony Optimization Algorithms
ERIC Educational Resources Information Center
Marcoulides, George A.; Drezner, Zvi
Ant colony optimization is a recently proposed heuristic procedure inspired by the behavior of real ants. This article applies the procedure to model specification searches in structural equation modeling and reports the results. The results demonstrate the capabilities of ant colony optimization algorithms for conducting automated searches.
Using neural networks to speed up optimization algorithms
M. Bazan; S. Russenschuck
The paper presents the application of Radial-basis-function (RBF) neural networks to speed up deterministic search algorithms used for the design and optimization of superconducting LHC magnets. The optimization of the iron yoke of the main dipoles requires a number of numerical field computations per trial solution as the field quality depends on the excitation of the magnets. This results in
Mesh Adaptive Direct Search Algorithms for Constrained Optimization
Charles Audet; J. E. Dennis Jr.
This paper introduces the Mesh Adaptive Direct Search (MADS) class of algorithms for nonlinear optimization. MADS extends the Generalized Pattern Search (GPS) class by allowing local exploration, called polling, in an asymptotically dense set of directions in the space of optimization variables. This means that under certain hypotheses, including a weak constraint qualification due to Rockafellar, MADS can treat constraints
Multiobjective Evolutionary Algorithm for Software Project Portfolio Optimization
optimization, Pro- ject portfolio management 1. INTRODUCTION Project selection problem (PSP) [1, 6 for the optimization. The complexity of the PSP is based on the often high number of projects from which a subset has portfolio should ad- here to. The PSP is NP-hard problem [3] so there is no exact algorithm that solves
Algorithmic funnel-and-gate system design optimization
Claudius M. B�rger; Peter Bayer; Michael Finkel
Funnel-and-gate systems (FGSs), which constitute a common variant of permeable reactive barriers used for in situ treatment of groundwater, pose particular challenges to the task of design optimization. Because of the complex interplay of funnels and gates, the evolutionary algorithms applied have to cope with multimodality, nonseparability, and nonlinearity of the optimization task. We analyze these features in a test
Genetic Algorithms for Optimal Scheduling of Chlorine Dosing in Water
Coello, Carlos A. Coello
for determining the optimal schedule of chlorine dosing within a water distribution system considering multiple-based method), is in progress. INTRODUCTION Controlling the levels of chlorine within the distribution systemoz343 Genetic Algorithms for Optimal Scheduling of Chlorine Dosing in Water Distribution Systems
A grid algorithm for bound constrained optimization of noisy functions
Neumaier, Arnold
Nelder- Mead in the noisy case. If performance is measured solely by the number of function evaluations in the optimization of experiments), the new algorithm is also significantly faster than Nelder-Mead. Revised version, February 1995 KEY WORDS: bound constrained optimization, noisy functions, Nelder-Mead method, quasi
Simplex Algorithm Math 364: Principles of Optimization, Lecture 8
Li, Haijun
... xn , b = b1 b2 ... bm Haijun Li Math 364: Principles of Optimization, Lecture 8 Spring = b1 b2 ... bm Note that some variables xis may be slack and/or excess variables. Haijun Li MathSimplex Algorithm Math 364: Principles of Optimization, Lecture 8 Haijun Li lih
Cruz, Frederico
-integer programming; G.2.2. [Discrete Mathematics]: Graph Theory-network problems; G.4.[Mathematics of ComputingPARALLEL ALGORITHMS FOR A MULTI-LEVEL NETWORK OPTIMIZATION PROBLEM F.R.B. CRUZa, * and G.R. MATEUSbMG, Brazil (Received 15 February 2000; In final form 12 March 2001) Multi-level network optimization (MLNO
Imperialist competitive algorithm combined with chaos for global optimization
NASA Astrophysics Data System (ADS)
Talatahari, S.; Farahmand Azar, B.; Sheikholeslami, R.; Gandomi, A. H.
A novel chaotic improved imperialist competitive algorithm (CICA) is presented for global optimization. The ICA is a new meta-heuristic optimization developed based on a socio-politically motivated strategy and contains two main steps: the movement of the colonies and the imperialistic competition. Here different chaotic maps are utilized to improve the movement step of the algorithm. Seven different chaotic maps are investigated and the Logistic and Sinusoidal maps are found as the best choices. Comparing the new algorithm with the other ICA-based methods demonstrates the superiority of the CICA for the benchmark functions.
Quantum-behaved particle swarm optimization algorithm for economic load dispatch of power system
Zhisheng Zhang
Quantum-behaved particle swarm optimization algorithm is firstly used in economic load dispatch of power system in this paper. Quantum-behaved particle swarm optimization algorithm is the integration of particle swarm optimization algorithm and quantum computing theory. The superposition characteristic and probability representation of quantum methodology are combined into particle swarm optimization algorithm. This can make a single particle be expressed by
Wishart, Michael; Ledwich, Gerard; Ghosh, Arindam [Queensland University of Technology, Brisbane, Queensland (Australia); Ivanovich, Grujica [Ergon Energy, Toowoomba, Queensland (Australia)
This paper describes the optimization of conductor size and the voltage regulator location and magnitude of long rural distribution lines. The optimization minimizes the lifetime cost of the lines, including capital costs and losses while observing voltage drop and operational constraints using a Genetic Algorithm (GA). The GA optimization is applied to a real Single Wire Earth Return (SWER) network in regional Queensland and results are presented.
sp3-hybridized framework structure of group-14 elements discovered by genetic algorithm
Nguyen, Manh Cuong [Ames Laboratory; Zhao, Xin [Ames Laboratory; Wang, Cai-Zhuang [Ames Laboratory; Ho, Kai-Ming [Ames Laboratory
Group-14 elements, including C, Si, Ge, and Sn, can form various stable and metastable structures. Finding new metastable structures of group-14 elements with desirable physical properties for new technological applications has attracted a lot of interest. Using a genetic algorithm, we discovered a new low-energy metastable distorted sp3-hybridized framework structure of the group-14 elements. It has P42/mnm symmetry with 12 atoms per unit cell. The void volume of this structure is as large as 139.7�3 for Si P42/mnm, and it can be used for gas or metal-atom encapsulation. Band-structure calculations show that P42/mnm structures of Si and Ge are semiconducting with energy band gaps close to the optimal values for optoelectronic or photovoltaic applications. With metal-atom encapsulation, the P42/mnm structure would also be a candidate for rattling-mediated superconducting or used as thermoelectric materials.
Ant Colony Learning Algorithm for Optimal Control
Jelmer Marinus van Ast; Robert Babuska; Bart De Schutter
\\u000a Ant colony optimization (ACO) is an optimization heuristic for solving combinatorial optimization problems and is inspired\\u000a by the swarming behavior of foraging ants. ACO has been successfully applied in various domains, such as routing and scheduling.\\u000a In particular, the agents, called ants here, are very efficient at sampling the problem space and quickly finding good solutions.\\u000a Motivated by the advantages
Air data system optimization using a genetic algorithm
NASA Technical Reports Server (NTRS)
Deshpande, Samir M.; Kumar, Renjith R.; Seywald, Hans; Siemers, Paul M., III
An optimization method for flush-orifice air data system design has been developed using the Genetic Algorithm approach. The optimization of the orifice array minimizes the effect of normally distributed random noise in the pressure readings on the calculation of air data parameters, namely, angle of attack, sideslip angle and freestream dynamic pressure. The optimization method is applied to the design of Pressure Distribution/Air Data System experiment (PD/ADS) proposed for inclusion in the Aeroassist Flight Experiment (AFE). Results obtained by the Genetic Algorithm method are compared to the results obtained by conventional gradient search method.
A Discrete Lagrangian Algorithm for Optimal Routing Problems
Kosmas, O. T.; Vlachos, D. S.; Simos, T. E. [University of Peloponnese, 22100 Tripoli (Greece)
The ideas of discrete Lagrangian methods for conservative systems are exploited for the construction of algorithms applicable in optimal ship routing problems. The algorithm presented here is based on the discretisation of Hamilton's principle of stationary action Lagrangian and specifically on the direct discretization of the Lagrange-Hamilton principle for a conservative system. Since, in contrast to the differential equations, the discrete Euler-Lagrange equations serve as constrains for the optimization of a given cost functional, in the present work we utilize this feature in order to minimize the cost function for optimal ship routing.
An Optimal Technology Mapping Algorithm for Delay Optimization in Lookup-Table Based FPGA Designs
Cong, Jason "Jingsheng"
An Optimal Technology Mapping Algorithm for Delay Optimization in Lookup-Table Based FPGA Designs-Map, that optimally solves the LUT-based FPGA technology mapping problem for depth minimization for general Boolean ASIC designs. The LUT-based FPGA is a popular archi- tecture used by several FPGA manufacturers
Optimization of Signal Processing Algorithms Raza Ahmed and Brian L. Evans
Evans, Brian L.
algorithms. Our prototype environment is written in Mathematica. 1 Introduction We optimize signal processingOptimization of Signal Processing Algorithms Raza Ahmed and Brian L. Evans razaa implementations of one-dimensional and multidimensional signal processing algorithms by rewriting subexpressions
Efficient algorithms for robustness in matroid optimization
Frederickson, G.N.; Solis-Oba, R. [Purdue Univ., West Layfayette, IN (United States)
The robustness function of a matroid measures the maximum increase in the weight of its minimum weight bases that can be obtained by increases of a given total cost on the weights of its elements. We present an algorithm for computing this function, that runs in strongly polynomial time for matroids in which independence can be tested in strongly polynomial time. We identify key properties of transversal, scheduling, and partition matroids, and exploit them to design robustness algorithms that are more efficient than our general algorithm.
Inverse planning optimization for hybrid prostate permanent-seed implant brachytherapy plans using two source strengths.
Cunha, J Adam M; Pickett, Barby; Pouliot, Jean
The purpose is to demonstrate the ability to generate clinically acceptable prostate permanent seed implant plans using two seed types which are identical except for their activity. The IPSA inverse planning algorithms were modified to include multiple dose matrices for the calculation of dose from different sources, and a selection algorithm was implemented to allow for the swapping of source type at any given source position. Five previously treated patients with a range of prostate volumes from 20-48 cm3 were re-optimized under two hybrid scenarios: (1) using 0.32 and 0.51 mGy m2 / h 125I, and (2) using 0.64 and 0.76 mGy m2 / h 125I. Isodose lines were generated and dosimetric indices , V150Prostate, D90Prostate, V150Urethra, V125Urethra, V120Urethra,V100Urethra, and D10Urethra were calculated. The algorithm allows for the generation of single-isotope, multi-activity hybrid brachytherapy plans. By dealing with only one radionuclide, but of different activity, the biology is unchanged from a standard plan. All V100Prostate were within 2.3 percentage points for every plan and always above the clinically desirable 95%. All V150Urethra were identically zero, and V120Urethra is always below the clinically acceptable value of 1.0 cm3. Clinical optimization times for the hybrid plans are still under one minute, for most cases. It is possible to generate clinically advantageous brachytherapy plans (i.e. obtain the same quality dose distribution as a standard single-activity plan) while incorporating leftover seeds from a previous patient treatment. This method will allow a clinic to continue to provide excellent patient care, but at a reduced cost. Multi-activity hybrid plans were equal in quality (as measured by the standard dosimetric indices) to plans with seeds of a single activity. Despite the expanded search space, optimization times for these studies were still under two minutes on a modern day laptop and can be reduced to below one minute in a clinical setting. With the typical cost of a set of PPI seeds on the order of thousands of dollars, it is possible to reduce the cost of brachytherapy treatments by allowing for easier use of seeds left over from a previous patient or unused due to a cancelled treatment. PMID:20717078
SAGA: a hybrid search algorithm for Bayesian Network structure learning of transcriptional regulatory networks.
Adabor, Emmanuel S; Acquaah-Mensah, George K; Oduro, Francis T
Bayesian Networks have been used for the inference of transcriptional regulatory relationships among genes, and are valuable for obtaining biological insights. However, finding optimal Bayesian Network (BN) is NP-hard. Thus, heuristic approaches have sought to effectively solve this problem. In this work, we develop a hybrid search method combining Simulated Annealing with a Greedy Algorithm (SAGA). SAGA explores most of the search space by undergoing a two-phase search: first with a Simulated Annealing search and then with a Greedy search. Three sets of background-corrected and normalized microarray datasets were used to test the algorithm. BN structure learning was also conducted using the datasets, and other established search methods as implemented in BANJO (Bayesian Network Inference with Java Objects). The Bayesian Dirichlet Equivalence (BDe) metric was used to score the networks produced with SAGA. SAGA predicted transcriptional regulatory relationships among genes in networks that evaluated to higher BDe scores with high sensitivities and specificities. Thus, the proposed method competes well with existing search algorithms for Bayesian Network structure learning of transcriptional regulatory networks. PMID:25181467
Statistically Optimal Combination of Algorithms Marek Petrik
Shenoy, Prashant
is with regard to a training set of weighted instances that represent the domain. This reserach has been supported in part by the grant VEGA 1/0131/03 #12;First, we define a framework in which the algorithms
Modeling and optimization of a hybrid solar combined cycle (HYCS)
NASA Astrophysics Data System (ADS)
Eter, Ahmad Adel
The main objective of this thesis is to investigate the feasibility of integrating concentrated solar power (CSP) technology with the conventional combined cycle technology for electric generation in Saudi Arabia. The generated electricity can be used locally to meet the annual increasing demand. Specifically, it can be utilized to meet the demand during the hours 10 am-3 pm and prevent blackout hours, of some industrial sectors. The proposed CSP design gives flexibility in the operation system. Since, it works as a conventional combined cycle during night time and it switches to work as a hybrid solar combined cycle during day time. The first objective of the thesis is to develop a thermo-economical mathematical model that can simulate the performance of a hybrid solar-fossil fuel combined cycle. The second objective is to develop a computer simulation code that can solve the thermo-economical mathematical model using available software such as E.E.S. The developed simulation code is used to analyze the thermo-economic performance of different configurations of integrating the CSP with the conventional fossil fuel combined cycle to achieve the optimal integration configuration. This optimal integration configuration has been investigated further to achieve the optimal design of the solar field that gives the optimal solar share. Thermo-economical performance metrics which are available in the literature have been used in the present work to assess the thermo-economic performance of the investigated configurations. The economical and environmental impact of integration CSP with the conventional fossil fuel combined cycle are estimated and discussed. Finally, the optimal integration configuration is found to be solarization steam side in conventional combined cycle with solar multiple 0.38 which needs 29 hectare and LEC of HYCS is 63.17 $/MWh under Dhahran weather conditions.
Series hybrid vehicles and optimized hydrogen engine design
NASA Astrophysics Data System (ADS)
Smith, J. R.; Aceves, S.; Vanblarigan, P.
Lawrence Livermore, Sandia Livermore and Los Alamos National Laboratories have a joint project to develop an optimized hydrogen fueled engine for series hybrid automobiles. The major divisions of responsibility are: system analysis, engine design and kinetics modeling by LLNL; performance and emission testing, and friction reduction by SNL; computational fluid mechanics and combustion modeling by LANL. This project is a component of the Department of Energy, Office of Utility Technology, National Hydrogen Program. We report here on the progress on system analysis and preliminary engine testing. We have done system studies of series hybrid automobiles that approach the PNGV design goal of 34 km/liter (80 mpg), for 384 km (240 mi) and 608 km (380 mi) ranges. Our results indicate that such a vehicle appears feasible using an optimized hydrogen engine. The impact of various on-board storage options on fuel economy are evaluated. Experiments with an available engine at the Sandia Combustion Research Facility demonstrated NO(x) emissions of 10 to 20 ppm at an equivalence ratio of 0.4, rising to about 500 ppm at 0.5 equivalence ratio using neat hydrogen. Hybrid vehicle simulation studies indicate that exhaust NO(x) concentrations must be less than 180 ppm to meet the 0.2 g/mile California Air Resources Board ULEV or Federal Tier-2 emissions regulations. We have designed and fabricated a first generation optimized hydrogen engine head for use on an existing single cylinder Onan engine. This head currently features 14.8:1 compression ratio, dual ignition, water cooling, two valves and open quiescent combustion chamber to minimize heat transfer losses.
Optimal Design for a Hybrid Ground-Source Heat Pump
Yu, Z.; Yuan, X.; Wang, B.
, the average entering fluid temperature (Tjp) to heat pump in situ should be achieved. 2) The relation between the exiting fluid temperature (Tcl )and outdoor wet bulb temperature (Ts) is described as: Tcl? Ts? 4? 3? 5?? . The cooling towers is more... strategy should be done . The size of GLHE and the capacity of the cooling tower is optimally designed based on the design procedures and control strategies under the condition of the heat balance in the ground in this paper. 2 HYBRID GSHPS...
Energy management of a fuel cell/ultracapacitor hybrid power system using an adaptive optimal-control method
NASA Astrophysics Data System (ADS)
Lin, Wei-Song; Zheng, Chen-Hong
Energy management of a fuel cell/ultracapacitor hybrid power system aims to optimize energy efficiency while satisfying the operational constraints. The current challenges include ensuring that the non-linear dynamics and energy management of a hybrid power system are consistent with state and input constraints imposed by operational limitations. This paper formulates the requirements for energy management of the hybrid power system as a constrained optimal-control problem, and then transforms the problem into an unconstrained form using the penalty-function method. Radial-basis-function networks are organized in an adaptive optimal-control algorithm to synthesize an optimal strategy for energy management. The obtained optimal strategy was verified in an electric vehicle powered by combining a fuel-cell system and an ultracapacitor bank. Driving-cycle tests were conducted to investigate the fuel consumption, fuel-cell peak power, and instantaneous rate of change in fuel-cell power. The results show that the energy efficiency of the electric vehicle is significantly improved relative to that without using the optimal strategy.
Optimal multisensor decision fusion of mine detection algorithms
NASA Astrophysics Data System (ADS)
Liao, Yuwei; Nolte, Loren W.; Collins, Leslie M.
Numerous detection algorithms, using various sensor modalities, have been developed for the detection of mines in cluttered and noisy backgrounds. The performance for each detection algorithm is typically reported in terms of the Receiver Operating Characteristic (ROC), which is a plot of the probability of detection versus false alarm as a function of the threshold setting on the output decision variable of each algorithm. In this paper we present multi-sensor decision fusion algorithms that combine the local decisions of existing detection algorithms for different sensors. This offers, in certain situations, an expedient, attractive and much simpler alternative to "starting over" with the redesign of a new algorithm which fuses multiple sensors at the data level. The goal in our multi-sensor decision fusion approach is to exploit complimentary strengths of existing multi-sensor algorithms so as to achieve performance (ROC) that exceeds the performance of any sensor algorithm operating in isolation. Our approach to multi-sensor decision fusion is based on optimal signal detection theory, using the likelihood ratio. We consider the optimal fusion of local decisions for two sensors, GPR (ground penetrating radar) and MD (metal detector). A new robust algorithm for decision fusion is presented that addresses the problem that the statistics of the training data is not likely to exactly match the statistics of the test data. ROC's are presented and compared for real data.
Comparative Evaluation of Different Optimization Algorithms for Structural Design Applications
NASA Technical Reports Server (NTRS)
Patnaik, Surya N.; Coroneos, Rula M.; Guptill, James D.; Hopkins, Dale A.
Non-linear programming algorithms play an important role in structural design optimization. Fortunately, several algorithms with computer codes are available. At NASA Lewis Research Centre, a project was initiated to assess the performance of eight different optimizers through the development of a computer code CometBoards. This paper summarizes the conclusions of that research. CometBoards was employed to solve sets of small, medium and large structural problems, using the eight different optimizers on a Cray-YMP8E/8128 computer. The reliability and efficiency of the optimizers were determined from the performance of these problems. For small problems, the performance of most of the optimizers could be considered adequate. For large problems, however, three optimizers (two sequential quadratic programming routines, DNCONG of IMSL and SQP of IDESIGN, along with Sequential Unconstrained Minimizations Technique SUMT) outperformed others. At optimum, most optimizers captured an identical number of active displacement and frequency constraints but the number of active stress constraints differed among the optimizers. This discrepancy can be attributed to singularity conditions in the optimization and the alleviation of this discrepancy can improve the efficiency of optimizers.
A Genetic Algorithm Approach to Multiple-Response Optimization
Ortiz, Francisco; Simpson, James R.; Pignatiello, Joseph J.; Heredia-Langner, Alejandro
Many designed experiments require the simultaneous optimization of multiple responses. A common approach is to use a desirability function combined with an optimization algorithm to find the most desirable settings of the controllable factors. However, as the problem grows even moderately in either the number of factors or the number of responses, conventional optimization algorithms can fail to find the global optimum. An alternative approach is to use a heuristic search procedure such as a genetic algorithm (GA). This paper proposes and develops a multiple-response solution technique using a GA in conjunction with an unconstrained desirability function. The GA requires that several parameters be determined in order for the algorithm to operate effectively. We perform a robust designed experiment in order to tune the genetic algorithm to perform well regardless of the complexity of the multiple-response optimization problem. The performance of the proposed GA method is evaluated and compared with the performance of the method that combines the desirability with the generalized reduced gradient (GRG) optimization. The evaluation shows that only the proposed GA approach consistently and effectively solves multiple-response problems of varying complexity.
A Solution Quality Assessment Method for Swarm Intelligence Optimization Algorithms
Wang, Gai-Ge; Zou, Kuansheng; Zhang, Jianhua
Nowadays, swarm intelligence optimization has become an important optimization tool and wildly used in many fields of application. In contrast to many successful applications, the theoretical foundation is rather weak. Therefore, there are still many problems to be solved. One problem is how to quantify the performance of algorithm in finite time, that is, how to evaluate the solution quality got by algorithm for practical problems. It greatly limits the application in practical problems. A solution quality assessment method for intelligent optimization is proposed in this paper. It is an experimental analysis method based on the analysis of search space and characteristic of algorithm itself. Instead of �value performance,� the �ordinal performance� is used as evaluation criteria in this method. The feasible solutions were clustered according to distance to divide solution samples into several parts. Then, solution space and �good enough� set can be decomposed based on the clustering results. Last, using relative knowledge of statistics, the evaluation result can be got. To validate the proposed method, some intelligent algorithms such as ant colony optimization (ACO), particle swarm optimization (PSO), and artificial fish swarm algorithm (AFS) were taken to solve traveling salesman problem. Computational results indicate the feasibility of proposed method. PMID:25013845