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. *Corresponding author: Ganesan T, School of Chemical Engineering, The University of Adelaide, Adelaide, 5005 South Australia, Australia; E-mail: [email protected] Received September 29, 2015; Accepted November 27, 2015; Published November 30, 2015 Citation: Ganesan T, Vasant P, Elamvazuthi I (2015) Non-Gaussian Random Generators in Bacteria Foraging Algorithm for Multiobjective Optimization. Ind Eng Manage 4: 182. doi:10.4172/2169-0316.1000182 Copyright: © 2015 Ganesan T, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.