Non-Gaussian Random Generators in Bacteria Foraging Algorithm for   Multiobjective Optimization
  Random generators or stochastic engines are a key component in the structure of metaheuristic algorithms. This work investigates the effects of non-Gaussian stochastic engines on the performance of metaheuristics when solving a real-world optimization problem. In this work, the bacteria foraging algorithm (BFA) was employed in tandem with four random generators (stochastic engines). The stochastic engines operate using the Weibull distribution, Gamma distribution, Gaussian distribution and a chaotic mechanism. The two non-Gaussian distributions are the Weibull and Gamma distributions. In this work, the approaches developed were implemented on the real-world multi-objective resin bonded sand mould problem. The Pareto frontiers obtained were benchmarked using two metrics; the hyper volume indicator (HVI) and the proposed Average Explorative Rate (AER) metric. Detail discussions from various perspectives on the effects of non-Gaussian random generators in metaheuristics are provided. 
Timothy Ganesan, Pandian Vasant and Irraivan Elamvazuthi
cs.AI