Hybrid particle swarm optimization with wavelet mutation and its industrial applications

Ling, SH; Iu, HHC; Chan, KY; Lam, HK; Yeung, BCW; Leung, FH

Hybrid particle swarm optimization with wavelet mutation and its industrial applications

Ling, SH

Iu, HHC Chan, KY Lam, HK Yeung, BCW Leung, FH

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2008, 38 (3), pp. 743 - 763
Issue Date:: 2008-06-01

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Field	Value	Language
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.contributor.author	Iu, HHC	en_US
dc.contributor.author	Chan, KY	en_US
dc.contributor.author	Lam, HK	en_US
dc.contributor.author	Yeung, BCW	en_US
dc.contributor.author	Leung, FH	en_US
dc.date.issued	2008-06-01	en_US
dc.identifier.citation	IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2008, 38 (3), pp. 743 - 763	en_US
dc.identifier.issn	1083-4419	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15255
dc.description.abstract	A new hybrid particle swarm optimization (PSO) that incorporates a wavelet-theory-based mutation operation is proposed. It applies the wavelet theory to enhance the PSO in exploring the solution space more effectively for a better solution. A suite of benchmark test functions and three industrial applications (solving the load flow problems, modeling the development of fluid dispensing for electronic packaging, and designing a neural-network-based controller) are employed to evaluate the performance and the applicability of the proposed method. Experimental results empirically show that the proposed method significantly outperforms the existing methods in terms of convergence speed, solution quality, and solution stability. © 2008 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics	en_US
dc.relation.isbasedon	10.1109/TSMCB.2008.921005	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.subject.mesh	Animals	en_US
dc.subject.mesh	Behavior, Animal	en_US
dc.subject.mesh	Biomimetics	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Neural Networks (Computer)	en_US
dc.subject.mesh	Models, Theoretical	en_US
dc.subject.mesh	Industry	en_US
dc.subject.mesh	Computer Simulation	en_US
dc.subject.mesh	Signal Processing, Computer-Assisted	en_US
dc.subject.mesh	Neural Networks, Computer	en_US
dc.title	Hybrid particle swarm optimization with wavelet mutation and its industrial applications	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	38	en_US
utslib.for	0913 Mechanical Engineering	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
dc.location.activity	WOS:000258763600014	en_US
dc.location.activity	San Diego, CA
pubs.embargo.period	Not known	en_US
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 Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	38	en_US

Abstract:

A new hybrid particle swarm optimization (PSO) that incorporates a wavelet-theory-based mutation operation is proposed. It applies the wavelet theory to enhance the PSO in exploring the solution space more effectively for a better solution. A suite of benchmark test functions and three industrial applications (solving the load flow problems, modeling the development of fluid dispensing for electronic packaging, and designing a neural-network-based controller) are employed to evaluate the performance and the applicability of the proposed method. Experimental results empirically show that the proposed method significantly outperforms the existing methods in terms of convergence speed, solution quality, and solution stability. © 2008 IEEE.

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http://hdl.handle.net/10453/15255