Chaotic Krill Herd algorithm

Wang, GG; Guo, L; Gandomi, AH; Hao, GS; Wang, H

Chaotic Krill Herd algorithm

Wang, GG Guo, L Gandomi, AH Hao, GS Wang, H

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Publisher:: ELSEVIER SCIENCE INC
Publication Type:: Journal Article
Citation:: Information Sciences, 2014, 274, pp. 17-34
Issue Date:: 2014-08-01

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Field	Value	Language
dc.contributor.author	Wang, GG
dc.contributor.author	Guo, L
dc.contributor.author	Gandomi, AH
dc.contributor.author	Hao, GS
dc.contributor.author	Wang, H
dc.date.accessioned	2023-03-28T21:09:31Z
dc.date.available	2023-03-28T21:09:31Z
dc.date.issued	2014-08-01
dc.identifier.citation	Information Sciences, 2014, 274, pp. 17-34
dc.identifier.issn	0020-0255
dc.identifier.issn	1872-6291
dc.identifier.uri	http://hdl.handle.net/10453/168748
dc.description.abstract	Recently, Gandomi and Alavi proposed a meta-heuristic optimization algorithm, called Krill Herd (KH). This paper introduces the chaos theory into the KH optimization process with the aim of accelerating its global convergence speed. Various chaotic maps are considered in the proposed chaotic KH (CKH) method to adjust the three main movements of the krill in the optimization process. Several test problems are utilized to evaluate the performance of CKH. The results show that the performance of CKH, with an appropriate chaotic map, is better than or comparable with the KH and other robust optimization approaches. © 2014 Elsevier Inc. All rights reserved.
dc.language	English
dc.publisher	ELSEVIER SCIENCE INC
dc.relation.ispartof	Information Sciences
dc.relation.isbasedon	10.1016/j.ins.2014.02.123
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	Chaotic Krill Herd algorithm
dc.type	Journal Article
utslib.citation.volume	274
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
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-28T21:09:29Z
pubs.publication-status	Published
pubs.volume	274

Abstract:

Recently, Gandomi and Alavi proposed a meta-heuristic optimization algorithm, called Krill Herd (KH). This paper introduces the chaos theory into the KH optimization process with the aim of accelerating its global convergence speed. Various chaotic maps are considered in the proposed chaotic KH (CKH) method to adjust the three main movements of the krill in the optimization process. Several test problems are utilized to evaluate the performance of CKH. The results show that the performance of CKH, with an appropriate chaotic map, is better than or comparable with the KH and other robust optimization approaches. © 2014 Elsevier Inc. All rights reserved.

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http://hdl.handle.net/10453/168748