Affiliations: [a] School of Chemical Engineering, The University of Adelaide, Adelaide, SA, Australia | [b] Department of Fundamental and Applied Sciences, Universiti Teknologi Petronas, Tronoh, Perak, Malaysia | [c] Department of Electrical and Electronics Engineering, Universiti Teknologi Petronas, Tronoh, Perak, Malaysia
Correspondence:
[*]
Corresponding author: T. Ganesan, School of Chemical Engineering, The University of Adelaide, Adelaide 5005 SA, Australia. E-mail:tim.ganesan@gmail.com
Abstract: In engineering optimization, multi-objective (MO) problems are frequently
encountered. In this work, a real-world MO problem (resin-bonded sand mould
system) is tackled using Particle Swarm Optimization (PSO) in conjunction
with the weighted-sum approach. Random generators (stochastic engines)
provides sufficient randomness for the algorithm during the search process.
The effects of non-Gaussian stochastic engines on the performance of the PSO
technique in a MO setting is explored in this work. 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. The Pareto frontiers obtained were
benchmarked using two metrics; the hypervolume indicator (HVI) and the
proposed Average Explorative Rate (AER) metric. Detail comparative analysis
on the effects of non-Gaussian random generators on the PSO technique is
provided.
