Chaotic cuckoo search

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

Chaotic cuckoo search

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

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Soft Computing, 2016, 20, (9), pp. 3349-3362
Issue Date:: 2016-09-01

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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	Zhang, Z
dc.contributor.author	Alavi, AH
dc.date.accessioned	2022-08-12T21:02:16Z
dc.date.available	2022-08-12T21:02:16Z
dc.date.issued	2016-09-01
dc.identifier.citation	Soft Computing, 2016, 20, (9), pp. 3349-3362
dc.identifier.issn	1432-7643
dc.identifier.issn	1433-7479
dc.identifier.uri	http://hdl.handle.net/10453/160050
dc.description.abstract	This study proposes a novel chaotic cuckoo search (CCS) optimization method by incorporating chaotic theory into cuckoo search (CS) algorithm. In CCS, chaos characteristics are combined with the CS with the intention of further enhancing its performance. Further, the elitism scheme is incorporated into CCS to preserve the best cuckoos. In CCS method, 12 chaotic maps are applied to tune the step size of the cuckoos used in the original CS method. Twenty-seven benchmark functions and an engineering case are utilized to investigate the efficiency of CCS. The results clearly demonstrate that the performance of CCS together with a suitable chaotic map is comparable as well as superior to that of the CS and other metaheuristic algorithms.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Soft Computing
dc.relation.isbasedon	10.1007/s00500-015-1726-1
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Chaotic cuckoo search
dc.type	Journal Article
utslib.citation.volume	20
utslib.location.activity	New Delhi, INDIA
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	1702 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	2022-08-12T21:02:15Z
pubs.issue	9
pubs.publication-status	Published
pubs.volume	20
utslib.citation.issue	9

Abstract:

This study proposes a novel chaotic cuckoo search (CCS) optimization method by incorporating chaotic theory into cuckoo search (CS) algorithm. In CCS, chaos characteristics are combined with the CS with the intention of further enhancing its performance. Further, the elitism scheme is incorporated into CCS to preserve the best cuckoos. In CCS method, 12 chaotic maps are applied to tune the step size of the cuckoos used in the original CS method. Twenty-seven benchmark functions and an engineering case are utilized to investigate the efficiency of CCS. The results clearly demonstrate that the performance of CCS together with a suitable chaotic map is comparable as well as superior to that of the CS and other metaheuristic algorithms.

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http://hdl.handle.net/10453/160050