Multiobjective optimization using particle swarm optimization with non-Gaussian random generators

Ganesan, T. and Vasant, P. and Elamvazuthi, I. (2016) Multiobjective optimization using particle swarm optimization with non-Gaussian random generators. Intelligent Decision Technologies, 10 (2). pp. 93-103.

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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. © 2016 IOS Press and the authors. All rights reserved.

Item Type: Article
Impact Factor: cited By 1
Uncontrolled Keywords: Engines; Gaussian distribution; Gaussian noise (electronic); Molds; Multiobjective optimization; Probability distributions; Resins; Stochastic systems; Weibull distribution, average explorative rate (AER); Hypervolume indicators; Multi objective; Non-gaussian randoms; Resin bonded sands, Particle swarm optimization (PSO)
Depositing User: Ms Sharifah Fahimah Saiyed Yeop
Date Deposited: 25 Mar 2022 08:53
Last Modified: 25 Mar 2022 08:53
URI: http://scholars.utp.edu.my/id/eprint/31041

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