A novel hybridization strategy for krill herd algorithm applied to clustering techniques

Abualigah, LM; Khader, AT; Hanandeh, ES; Gandomi, AH

A novel hybridization strategy for krill herd algorithm applied to clustering techniques

Abualigah, LM Khader, AT Hanandeh, ES Gandomi, AH

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Applied Soft Computing, 2017, 60, pp. 423-435
Issue Date:: 2017-11-01

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Field	Value	Language
dc.contributor.author	Abualigah, LM
dc.contributor.author	Khader, AT
dc.contributor.author	Hanandeh, ES
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2022-08-25T01:43:19Z
dc.date.available	2022-08-25T01:43:19Z
dc.date.issued	2017-11-01
dc.identifier.citation	Applied Soft Computing, 2017, 60, pp. 423-435
dc.identifier.issn	1568-4946
dc.identifier.issn	1872-9681
dc.identifier.uri	http://hdl.handle.net/10453/160815
dc.description.abstract	Krill herd (KH) is a stochastic nature-inspired optimization algorithm that has been successfully used to solve numerous complex optimization problems. This paper proposed a novel hybrid of KH algorithm with harmony search (HS) algorithm, namely, H-KHA, to improve the global (diversification) search ability. The enhancement includes adding global search operator (improvise a new solution) of the HS algorithm to the KH algorithm for improving the exploration search ability by a new probability factor, namely, Distance factor, thereby moving krill individuals toward the best global solution. The effectiveness of the proposed H-KHA is tested on seven standard datasets from the UCI Machine Learning Repository that are commonly used in the domain of data clustering, also six common text datasets that are used in the domain of text document clustering. The experiments reveal that the proposed hybrid KHA with HS algorithm (H-KHA) enhanced the results in terms of accurate clusters and high convergence rate. Mostly, the performance of H-KHA is superior or at least highly competitive with the original KH algorithm, well-known clustering techniques and other comparative optimization algorithms.
dc.language	English
dc.publisher	Elsevier
dc.relation.ispartof	Applied Soft Computing
dc.relation.isbasedon	10.1016/j.asoc.2017.06.059
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0806 Information Systems
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	A novel hybridization strategy for krill herd algorithm applied to clustering techniques
dc.type	Journal Article
utslib.citation.volume	60
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
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	*
pubs.consider-herdc	false
dc.date.updated	2022-08-25T01:43:18Z
pubs.publication-status	Published
pubs.volume	60

Abstract:

Krill herd (KH) is a stochastic nature-inspired optimization algorithm that has been successfully used to solve numerous complex optimization problems. This paper proposed a novel hybrid of KH algorithm with harmony search (HS) algorithm, namely, H-KHA, to improve the global (diversification) search ability. The enhancement includes adding global search operator (improvise a new solution) of the HS algorithm to the KH algorithm for improving the exploration search ability by a new probability factor, namely, Distance factor, thereby moving krill individuals toward the best global solution. The effectiveness of the proposed H-KHA is tested on seven standard datasets from the UCI Machine Learning Repository that are commonly used in the domain of data clustering, also six common text datasets that are used in the domain of text document clustering. The experiments reveal that the proposed hybrid KHA with HS algorithm (H-KHA) enhanced the results in terms of accurate clusters and high convergence rate. Mostly, the performance of H-KHA is superior or at least highly competitive with the original KH algorithm, well-known clustering techniques and other comparative optimization algorithms.

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http://hdl.handle.net/10453/160815