An effective krill herd algorithm with migration operator in biogeography-based optimization

Wang, GG; Gandomi, AH; Alavi, AH

An effective krill herd algorithm with migration operator in biogeography-based optimization

Wang, GG Gandomi, AH Alavi, AH

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Publisher:: ELSEVIER SCIENCE INC
Publication Type:: Journal Article
Citation:: Applied Mathematical Modelling, 2014, 38, (9-10), pp. 2454-2462
Issue Date:: 2014-05-01

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Field	Value	Language
dc.contributor.author	Wang, GG
dc.contributor.author	Gandomi, AH
dc.contributor.author	Alavi, AH
dc.date.accessioned	2023-03-28T21:18:55Z
dc.date.available	2023-03-28T21:18:55Z
dc.date.issued	2014-05-01
dc.identifier.citation	Applied Mathematical Modelling, 2014, 38, (9-10), pp. 2454-2462
dc.identifier.issn	0307-904X
dc.identifier.issn	1872-8480
dc.identifier.uri	http://hdl.handle.net/10453/168750
dc.description.abstract	Krill herd (KH) is a novel search heuristic method. To improve its performance, a biogeography-based krill herd (BBKH) algorithm is presented for solving complex optimization tasks. The improvement involves introducing a new krill migration (KM) operator when the krill updating to deal with optimization problems more efficiently. The KM operator emphasizes the exploitation and lets the krill cluster around the best solutions at the later run phase of the search. The effects of these enhancements are tested by various well-defined benchmark functions. Based on the experimental results, this novel BBKH approach performs better than the basic KH and other optimization algorithms. © 2013 Elsevier Inc.
dc.language	English
dc.publisher	ELSEVIER SCIENCE INC
dc.relation.ispartof	Applied Mathematical Modelling
dc.relation.isbasedon	10.1016/j.apm.2013.10.052
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0103 Numerical and Computational Mathematics, 0801 Artificial Intelligence and Image Processing
dc.subject.classification	Mechanical Engineering & Transports
dc.title	An effective krill herd algorithm with migration operator in biogeography-based optimization
dc.type	Journal Article
utslib.citation.volume	38
utslib.for	0102 Applied Mathematics
utslib.for	0103 Numerical and Computational Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
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	2023-03-28T21:18:54Z
pubs.issue	9-10
pubs.publication-status	Published
pubs.volume	38
utslib.citation.issue	9-10

Abstract:

Krill herd (KH) is a novel search heuristic method. To improve its performance, a biogeography-based krill herd (BBKH) algorithm is presented for solving complex optimization tasks. The improvement involves introducing a new krill migration (KM) operator when the krill updating to deal with optimization problems more efficiently. The KM operator emphasizes the exploitation and lets the krill cluster around the best solutions at the later run phase of the search. The effects of these enhancements are tested by various well-defined benchmark functions. Based on the experimental results, this novel BBKH approach performs better than the basic KH and other optimization algorithms. © 2013 Elsevier Inc.

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http://hdl.handle.net/10453/168750