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Particle Swarm Optimisation with Improved Learning Strategy

In this paper, a new variant of particle swarm optimisation (PSO) called PSO with improved learning strategy (PSO-ILS) is developed. Specifically, an ILS module is proposed to generate a more effective and efficient exemplar, which could offer a more promising search direction to the PSO-ILS part...

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Main Authors: Wei , Hong Lim, Isa, Nor Ashidi Mat
Format: Article
Language:English
Published: Taylor's University 2015
Subjects:
TA1-2040 Engineering (General). Civil engineering (General)
Online Access:http://eprints.usm.my/42783/1/JES_Vol._11_2015_-_Art._4%2827-48%29.pdf
http://eprints.usm.my/42783/
http://web.usm.my/jes/11_2015/JES%20Vol.%2011%202015%20-%20Art.%204(27-48).pdf
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spelling my.usm.eprints.42783 http://eprints.usm.my/42783/ Particle Swarm Optimisation with Improved Learning Strategy Wei , Hong Lim Isa, Nor Ashidi Mat TA1-2040 Engineering (General). Civil engineering (General) In this paper, a new variant of particle swarm optimisation (PSO) called PSO with improved learning strategy (PSO-ILS) is developed. Specifically, an ILS module is proposed to generate a more effective and efficient exemplar, which could offer a more promising search direction to the PSO-ILS particle. Comparison is made on the PSO-ILS with 6 well-established PSO variants on 10 benchmark functions to investigate the optimisation capability of the proposed algorithm. The simulation results reveal that PSO-ILS outperforms its peers for the majority of the tested benchmarks by demonstrating superior search accuracy, reliability and efficiency. Taylor's University 2015 Article PeerReviewed application/pdf en http://eprints.usm.my/42783/1/JES_Vol._11_2015_-_Art._4%2827-48%29.pdf Wei , Hong Lim and Isa, Nor Ashidi Mat (2015) Particle Swarm Optimisation with Improved Learning Strategy. Journal of Engineering Science and Technology, 11. pp. 27-48. ISSN 1823-4690 http://web.usm.my/jes/11_2015/JES%20Vol.%2011%202015%20-%20Art.%204(27-48).pdf
institution Universiti Sains Malaysia
building Hamzah Sendut Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
url_provider http://eprints.usm.my/
language English
topic TA1-2040 Engineering (General). Civil engineering (General)
spellingShingle TA1-2040 Engineering (General). Civil engineering (General)
Wei , Hong Lim
Isa, Nor Ashidi Mat
Particle Swarm Optimisation with Improved Learning Strategy
description In this paper, a new variant of particle swarm optimisation (PSO) called PSO with improved learning strategy (PSO-ILS) is developed. Specifically, an ILS module is proposed to generate a more effective and efficient exemplar, which could offer a more promising search direction to the PSO-ILS particle. Comparison is made on the PSO-ILS with 6 well-established PSO variants on 10 benchmark functions to investigate the optimisation capability of the proposed algorithm. The simulation results reveal that PSO-ILS outperforms its peers for the majority of the tested benchmarks by demonstrating superior search accuracy, reliability and efficiency.
format Article
author Wei , Hong Lim
Isa, Nor Ashidi Mat
author_facet Wei , Hong Lim
Isa, Nor Ashidi Mat
author_sort Wei , Hong Lim
title Particle Swarm Optimisation with Improved Learning Strategy
title_short Particle Swarm Optimisation with Improved Learning Strategy
title_full Particle Swarm Optimisation with Improved Learning Strategy
title_fullStr Particle Swarm Optimisation with Improved Learning Strategy
title_full_unstemmed Particle Swarm Optimisation with Improved Learning Strategy
title_sort particle swarm optimisation with improved learning strategy
publisher Taylor's University
publishDate 2015
url http://eprints.usm.my/42783/1/JES_Vol._11_2015_-_Art._4%2827-48%29.pdf
http://eprints.usm.my/42783/
http://web.usm.my/jes/11_2015/JES%20Vol.%2011%202015%20-%20Art.%204(27-48).pdf
_version_ 1643710574712848384
score 13.211869

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