Vector coevolving particle swarm optimization algorithm

Zhang, Q; Liu, W; Meng, X; Yang, B; Vasilakos, AV

Vector coevolving particle swarm optimization algorithm

Zhang, Q Liu, W Meng, X Yang, B Vasilakos, AV

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Information Sciences, 2017, 394-395, pp. 273-298
Issue Date:: 2017-07-01

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Field	Value	Language
dc.contributor.author	Zhang, Q
dc.contributor.author	Liu, W
dc.contributor.author	Meng, X
dc.contributor.author	Yang, B
dc.contributor.author	Vasilakos, AV
dc.date.accessioned	2022-08-24T04:20:01Z
dc.date.available	2022-08-24T04:20:01Z
dc.date.issued	2017-07-01
dc.identifier.citation	Information Sciences, 2017, 394-395, pp. 273-298
dc.identifier.issn	0020-0255
dc.identifier.issn	1872-6291
dc.identifier.uri	http://hdl.handle.net/10453/160779
dc.description.abstract	In this paper, we propose a novel vector coevolving particle swarm optimization algorithm (VCPSO). In VCPSO, the full dimension of each particle is first randomly partitioned into several sub-dimensions. Then, we randomly assign either one of our newly designed scalar operators or learning operators to update the values in each sub-dimension. The scalar operators are designed to enhance the population diversity and avoid premature convergence. In addition, the learning operators are designed to enhance the global and local search ability. The proposed algorithm is compared with several other classical swarm optimizers on thirty-three benchmark functions. Comprehensive experimental results show that VCPSO displays a better or comparable performance compared to the other algorithms in terms of solution accuracy and statistical results.
dc.language	English
dc.publisher	Elsevier
dc.relation.ispartof	Information Sciences
dc.relation.isbasedon	10.1016/j.ins.2017.01.038
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Vector coevolving particle swarm optimization algorithm
dc.type	Journal Article
utslib.citation.volume	394-395
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-08-24T04:19:56Z
pubs.publication-status	Published
pubs.volume	394-395

Abstract:

In this paper, we propose a novel vector coevolving particle swarm optimization algorithm (VCPSO). In VCPSO, the full dimension of each particle is first randomly partitioned into several sub-dimensions. Then, we randomly assign either one of our newly designed scalar operators or learning operators to update the values in each sub-dimension. The scalar operators are designed to enhance the population diversity and avoid premature convergence. In addition, the learning operators are designed to enhance the global and local search ability. The proposed algorithm is compared with several other classical swarm optimizers on thirty-three benchmark functions. Comprehensive experimental results show that VCPSO displays a better or comparable performance compared to the other algorithms in terms of solution accuracy and statistical results.

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http://hdl.handle.net/10453/160779