A new improved krill herd algorithm for global numerical optimization

Guo, L; Wang, GG; Gandomi, AH; Alavi, AH; Duan, H

A new improved krill herd algorithm for global numerical optimization

Guo, L Wang, GG Gandomi, AH Alavi, AH Duan, H

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Neurocomputing, 2014, 138, pp. 392-402
Issue Date:: 2014-08-22

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Field	Value	Language
dc.contributor.author	Guo, L
dc.contributor.author	Wang, GG
dc.contributor.author	Gandomi, AH
dc.contributor.author	Alavi, AH
dc.contributor.author	Duan, H
dc.date.accessioned	2023-03-28T21:08:12Z
dc.date.available	2023-03-28T21:08:12Z
dc.date.issued	2014-08-22
dc.identifier.citation	Neurocomputing, 2014, 138, pp. 392-402
dc.identifier.issn	0925-2312
dc.identifier.issn	1872-8286
dc.identifier.uri	http://hdl.handle.net/10453/168747
dc.description.abstract	This study presents an improved krill herd (IKH) approach to solve global optimization problems. The main improvement pertains to the exchange of information between top krill during motion calculation process to generate better candidate solutions. Furthermore, the proposed IKH method uses a new Lévy flight distribution and elitism scheme to update the KH motion calculation. This novel meta-heuristic approach can accelerate the global convergence speed while preserving the robustness of the basic KH algorithm. Besides, the detailed implementation procedure for the IKH method is described. Several standard benchmark functions are used to verify the efficiency of IKH. Based on the results, the performance of IKH is superior to or highly competitive with the standard KH and other robust population-based optimization methods. © 2014 Elsevier B.V.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Neurocomputing
dc.relation.isbasedon	10.1016/j.neucom.2014.01.023
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 17 Psychology and Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	A new improved krill herd algorithm for global numerical optimization
dc.type	Journal Article
utslib.citation.volume	138
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	in_progress	*
dc.date.updated	2023-03-28T21:08:11Z
pubs.publication-status	Published
pubs.volume	138

Abstract:

This study presents an improved krill herd (IKH) approach to solve global optimization problems. The main improvement pertains to the exchange of information between top krill during motion calculation process to generate better candidate solutions. Furthermore, the proposed IKH method uses a new Lévy flight distribution and elitism scheme to update the KH motion calculation. This novel meta-heuristic approach can accelerate the global convergence speed while preserving the robustness of the basic KH algorithm. Besides, the detailed implementation procedure for the IKH method is described. Several standard benchmark functions are used to verify the efficiency of IKH. Based on the results, the performance of IKH is superior to or highly competitive with the standard KH and other robust population-based optimization methods. © 2014 Elsevier B.V.

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http://hdl.handle.net/10453/168747