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

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) provi...

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Main Authors: Ganesan, T., Vasant, P., Elamvazuthi, I.
Format: Article
Published: IOS Press 2016
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960855166&doi=10.3233%2fIDT-150241&partnerID=40&md5=c89109371acc60ceb0b48c8ded51e8bc
http://eprints.utp.edu.my/30890/
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spelling my.utp.eprints.308902022-03-25T07:40:27Z Multiobjective optimization using particle swarm optimization with non-Gaussian random generators Ganesan, T. Vasant, P. Elamvazuthi, I. 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. IOS Press 2016 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960855166&doi=10.3233%2fIDT-150241&partnerID=40&md5=c89109371acc60ceb0b48c8ded51e8bc 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. http://eprints.utp.edu.my/30890/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description 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.
format Article
author Ganesan, T.
Vasant, P.
Elamvazuthi, I.
spellingShingle Ganesan, T.
Vasant, P.
Elamvazuthi, I.
Multiobjective optimization using particle swarm optimization with non-Gaussian random generators
author_facet Ganesan, T.
Vasant, P.
Elamvazuthi, I.
author_sort Ganesan, T.
title Multiobjective optimization using particle swarm optimization with non-Gaussian random generators
title_short Multiobjective optimization using particle swarm optimization with non-Gaussian random generators
title_full Multiobjective optimization using particle swarm optimization with non-Gaussian random generators
title_fullStr Multiobjective optimization using particle swarm optimization with non-Gaussian random generators
title_full_unstemmed Multiobjective optimization using particle swarm optimization with non-Gaussian random generators
title_sort multiobjective optimization using particle swarm optimization with non-gaussian random generators
publisher IOS Press
publishDate 2016
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960855166&doi=10.3233%2fIDT-150241&partnerID=40&md5=c89109371acc60ceb0b48c8ded51e8bc
http://eprints.utp.edu.my/30890/
_version_ 1738657171084673024
score 13.244368

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