A hybrid method based on krill herd and quantum-behaved particle swarm optimization

Wang, GG; Gandomi, AH; Alavi, AH; Deb, S

A hybrid method based on krill herd and quantum-behaved particle swarm optimization

Wang, GG Gandomi, AH Alavi, AH Deb, S

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Publisher:: SPRINGER LONDON LTD
Publication Type:: Journal Article
Citation:: Neural Computing and Applications, 2016, 27, (4), pp. 989-1006
Issue Date:: 2016-05-01

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Field	Value	Language
dc.contributor.author	Wang, GG
dc.contributor.author	Gandomi, AH
dc.contributor.author	Alavi, AH
dc.contributor.author	Deb, S
dc.date.accessioned	2022-08-12T02:34:46Z
dc.date.available	2022-08-12T02:34:46Z
dc.date.issued	2016-05-01
dc.identifier.citation	Neural Computing and Applications, 2016, 27, (4), pp. 989-1006
dc.identifier.issn	0941-0643
dc.identifier.issn	1433-3058
dc.identifier.uri	http://hdl.handle.net/10453/160005
dc.description.abstract	A novel hybrid Krill herd (KH) and quantum-behaved particle swarm optimization (QPSO), called KH–QPSO, is presented for benchmark and engineering optimization. QPSO is intended for enhancing the ability of the local search and increasing the individual diversity in the population. KH–QPSO is capable of avoiding the premature convergence and eventually finding the function minimum; especially, KH–QPSO can make all the individuals proceed to the true global optimum without introducing additional operators to the basic KH and QPSO algorithms. To verify its performance, various experiments are carried out on an array of test problems as well as an engineering case. Based on the results, we can easily infer that the hybrid KH–QPSO is more efficient than other optimization methods for solving standard test problems and engineering optimization problems.
dc.language	English
dc.publisher	SPRINGER LONDON LTD
dc.relation.ispartof	Neural Computing and Applications
dc.relation.isbasedon	10.1007/s00521-015-1914-z
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	A hybrid method based on krill herd and quantum-behaved particle swarm optimization
dc.type	Journal Article
utslib.citation.volume	27
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2022-08-12T02:34:45Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	27
utslib.citation.issue	4

Abstract:

A novel hybrid Krill herd (KH) and quantum-behaved particle swarm optimization (QPSO), called KH–QPSO, is presented for benchmark and engineering optimization. QPSO is intended for enhancing the ability of the local search and increasing the individual diversity in the population. KH–QPSO is capable of avoiding the premature convergence and eventually finding the function minimum; especially, KH–QPSO can make all the individuals proceed to the true global optimum without introducing additional operators to the basic KH and QPSO algorithms. To verify its performance, various experiments are carried out on an array of test problems as well as an engineering case. Based on the results, we can easily infer that the hybrid KH–QPSO is more efficient than other optimization methods for solving standard test problems and engineering optimization problems.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/160005