A comprehensive review of krill herd algorithm: variants, hybrids and applications

Wang, GG; Gandomi, AH; Alavi, AH; Gong, D

A comprehensive review of krill herd algorithm: variants, hybrids and applications

Wang, GG Gandomi, AH

Alavi, AH Gong, D

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Publication Type:: Journal Article
Citation:: Artificial Intelligence Review, 2019, 51 (1), pp. 119 - 148
Issue Date:: 2019-01-31

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Field	Value	Language
dc.contributor.author	Wang, GG	en_US
dc.contributor.author	Gandomi, AH https://orcid.org/0000-0002-2798-0104	en_US
dc.contributor.author	Alavi, AH	en_US
dc.contributor.author	Gong, D	en_US
dc.date.issued	2019-01-31	en_US
dc.identifier.citation	Artificial Intelligence Review, 2019, 51 (1), pp. 119 - 148	en_US
dc.identifier.issn	0269-2821	en_US
dc.identifier.uri	http://hdl.handle.net/10453/138309
dc.description.abstract	© 2017, Springer Science+Business Media Dordrecht. Krill herd (KH) is a novel swarm-based metaheuristic optimization algorithm inspired by the krill herding behavior. The objective function in the KH optimization process is based on the least distance between the food location and position of a krill. The KH method has been proven to outperform several state-of-the-art metaheuristic algorithms on many benchmarks and engineering cases. This paper presents a comprehensive review of different versions of the KH algorithm and their engineering applications. The study is divided into the following general parts: KH variants, engineering optimization/application, and theoretical analysis. In addition, specific features of KH and future directions are discussed.	en_US
dc.relation.ispartof	Artificial Intelligence Review	en_US
dc.relation.isbasedon	10.1007/s10462-017-9559-1	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A comprehensive review of krill herd algorithm: variants, hybrids and applications	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	51	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1702 Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	51	en_US

Abstract:

© 2017, Springer Science+Business Media Dordrecht. Krill herd (KH) is a novel swarm-based metaheuristic optimization algorithm inspired by the krill herding behavior. The objective function in the KH optimization process is based on the least distance between the food location and position of a krill. The KH method has been proven to outperform several state-of-the-art metaheuristic algorithms on many benchmarks and engineering cases. This paper presents a comprehensive review of different versions of the KH algorithm and their engineering applications. The study is divided into the following general parts: KH variants, engineering optimization/application, and theoretical analysis. In addition, specific features of KH and future directions are discussed.

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http://hdl.handle.net/10453/138309