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.
