Opposition-based krill herd algorithm with Cauchy mutation and position clamping

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

Opposition-based krill herd algorithm with Cauchy mutation and position clamping

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

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Neurocomputing, 2016, 177, pp. 147-157
Issue Date:: 2016-02-12

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Field	Value	Language
dc.contributor.author	Wang, GG
dc.contributor.author	Deb, S
dc.contributor.author	Gandomi, AH
dc.contributor.author	Alavi, AH
dc.date.accessioned	2022-08-12T20:27:43Z
dc.date.available	2022-08-12T20:27:43Z
dc.date.issued	2016-02-12
dc.identifier.citation	Neurocomputing, 2016, 177, pp. 147-157
dc.identifier.issn	0925-2312
dc.identifier.issn	1872-8286
dc.identifier.uri	http://hdl.handle.net/10453/160042
dc.description.abstract	Krill herd (KH) has been proven to be an efficient algorithm for function optimization. For some complex functions, this algorithm may have problems with convergence or being trapped in local minima. To cope with these issues, this paper presents an improved KH-based algorithm, called Opposition Krill Herd (OKH). The proposed approach utilizes opposition-based learning (OBL), position clamping (PC) and Cauchy mutation (CM) to enhance the performance of basic KH. OBL accelerates the convergence of the method while both PC and heavy-tailed CM help KH escape from local optima. Simulations are implemented on an array of benchmark functions and two engineering optimization problems. The results show that OKH has a good performance on majority of the considered functions and two engineering cases. The influence of each individual strategy (OBL, CM and PC) on KH is verified through 25 benchmarks. The results show that the KH with OBL, CM and PC operators, has the best performance among different variants of OKH.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Neurocomputing
dc.relation.isbasedon	10.1016/j.neucom.2015.11.018
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 17 Psychology and Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Opposition-based krill herd algorithm with Cauchy mutation and position clamping
dc.type	Journal Article
utslib.citation.volume	177
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	17 Psychology and 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	2022-08-12T20:27:42Z
pubs.publication-status	Published
pubs.volume	177

Abstract:

Krill herd (KH) has been proven to be an efficient algorithm for function optimization. For some complex functions, this algorithm may have problems with convergence or being trapped in local minima. To cope with these issues, this paper presents an improved KH-based algorithm, called Opposition Krill Herd (OKH). The proposed approach utilizes opposition-based learning (OBL), position clamping (PC) and Cauchy mutation (CM) to enhance the performance of basic KH. OBL accelerates the convergence of the method while both PC and heavy-tailed CM help KH escape from local optima. Simulations are implemented on an array of benchmark functions and two engineering optimization problems. The results show that OKH has a good performance on majority of the considered functions and two engineering cases. The influence of each individual strategy (OBL, CM and PC) on KH is verified through 25 benchmarks. The results show that the KH with OBL, CM and PC operators, has the best performance among different variants of OKH.

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http://hdl.handle.net/10453/160042