A multi-stage krill herd algorithm for global numerical optimization

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

A multi-stage krill herd algorithm for global numerical optimization

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

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Publisher:: WORLD SCIENTIFIC PUBL CO PTE LTD
Publication Type:: Journal Article
Citation:: International Journal on Artificial Intelligence Tools, 2016, 25, (2)
Issue Date:: 2016-04-01

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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	2022-08-15T19:49:49Z
dc.date.available	2022-08-15T19:49:49Z
dc.date.issued	2016-04-01
dc.identifier.citation	International Journal on Artificial Intelligence Tools, 2016, 25, (2)
dc.identifier.issn	0218-2130
dc.identifier.issn	1793-6349
dc.identifier.uri	http://hdl.handle.net/10453/160239
dc.description.abstract	A multi-stage krill herd (MSKH) algorithm is presented to fully exploit the global and local search abilities of the standard krill herd (KH) optimization method. The proposed method involves exploration and exploitation stages. The exploration stage uses the basic KH algorithm to select a good candidate solution set. This phase is followed by fine-tuning a good candidate solution in the exploitation stage with a focused local mutation and crossover (LMC) operator in order to enhance the reliability of the method for solving global numerical optimization problems. Moreover, the elitism scheme is introduced into the MSKH method to guarantee the best solution. The performance of MSKH is verified using twenty-five standard and rotated and shifted benchmark problems. The results show the superiority of the proposed algorithm to the standard KH and other well-known optimization methods.
dc.language	English
dc.publisher	WORLD SCIENTIFIC PUBL CO PTE LTD
dc.relation.ispartof	International Journal on Artificial Intelligence Tools
dc.relation.isbasedon	10.1142/S021821301550030X
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0802 Computation Theory and Mathematics, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	A multi-stage krill herd algorithm for global numerical optimization
dc.type	Journal Article
utslib.citation.volume	25
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0802 Computation Theory and Mathematics
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	2022-08-15T19:49:48Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	25
utslib.citation.issue	2

Abstract:

A multi-stage krill herd (MSKH) algorithm is presented to fully exploit the global and local search abilities of the standard krill herd (KH) optimization method. The proposed method involves exploration and exploitation stages. The exploration stage uses the basic KH algorithm to select a good candidate solution set. This phase is followed by fine-tuning a good candidate solution in the exploitation stage with a focused local mutation and crossover (LMC) operator in order to enhance the reliability of the method for solving global numerical optimization problems. Moreover, the elitism scheme is introduced into the MSKH method to guarantee the best solution. The performance of MSKH is verified using twenty-five standard and rotated and shifted benchmark problems. The results show the superiority of the proposed algorithm to the standard KH and other well-known optimization methods.

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http://hdl.handle.net/10453/160239