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Dynamic Parameter Design by Ant Colony Optimization and Neural Networks

Parameter design is the most important phase in the development of new products and processes, especially in regards to dynamic systems. Statistics-based approaches are usually employed to address dynamic parameter design problems; however, these approaches have some limitations when applied to dynamic systems with continuous control factors. This study proposes a novel three-phase approach for resolving the dynamic parameter design problems as well as the static characteristic problems, which combines continuous ant colony optimisation (CACO) with neural networks. The proposed approach trains a neural network model to construct the relationship function among response, inputs and parameters of a dynamic system, which is then used to predict the responses of the system. Three performance functions are developed to evaluate the fitness of the predicted responses. The best parameter settings can be obtained by performing a CACO algorithm according to the fitness value. The best parameter settings that are obtained are no longer restricted to the values of control factor levels. The proposed approach is demonstrated with two illustrative examples. Results show that the proposed approach outperforms the Taguchi method.