Self adaptive cuckoo search: Analysis and experimentation

Salgotra, R; Singh, U; Saha, S; Gandomi, AH

Self adaptive cuckoo search: Analysis and experimentation

Salgotra, R Singh, U Saha, S Gandomi, AH

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Swarm and Evolutionary Computation, 2021, 60, (100751), pp. 1-15
Issue Date:: 2021-02-01

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Field	Value	Language
dc.contributor.author	Salgotra, R
dc.contributor.author	Singh, U
dc.contributor.author	Saha, S
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2022-03-31T02:53:18Z
dc.date.available	2022-03-31T02:53:18Z
dc.date.issued	2021-02-01
dc.identifier.citation	Swarm and Evolutionary Computation, 2021, 60, (100751), pp. 1-15
dc.identifier.issn	2210-6502
dc.identifier.issn	2210-6510
dc.identifier.uri	http://hdl.handle.net/10453/155751
dc.description.abstract	Cuckoo search (CS) algorithm, based on the brood parasitic behaviour of cuckoo species, since its inception, has proved its worth in various fields of research and can be considered as an efficient problem solver. Though CS is a very good algorithm, its performance degrades as the problem complexity increases. So a new version namely self-adaptive CS (SACS) is proposed to improve its performance. The algorithm employs adaptive parameters and hence no parameter tuning is required to be done. Here proportional population reduction based on the fitness of current best and previous best solution is followed. Secondly, in order to improve the exploration and exploitation tendencies, the Gaussian sampling mechanism known as bare-bones variant along with division of population and generations is added. The concept of Weibull distributed probability switching is also added to increase the balance between the exploration and exploitation processes. Further, CS as an extension of differential evolution (DE), genetic algorithm (GA), stability analysis with respect to Von Neumann’s stability criteria are also presented. For performance evaluation, the SACS algorithm is tested on CEC 2017 benchmark problems and compared with respect to self-adaptive DE (SaDE), JADE, success-history based adaptive DE (SHADE), SHADE with linear population size reduction (LSHADE), mean-variance mapping optimization (MVMO), cuckoo version 1.0 (CV1.0), cuckoo version new (CVnew) and others. Experimental results and statistical analysis show that the proposed SACS algorithm performs better than SaDE, JADE, CV1.0, CVnew and CS whereas comparable with respect to LSHADE, SHADE, and MVMO. Convergence profiles further validate the results.
dc.language	English
dc.publisher	Elsevier
dc.relation.ispartof	Swarm and Evolutionary Computation
dc.relation.isbasedon	10.1016/j.swevo.2020.100751
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0802 Computation Theory and Mathematics
dc.title	Self adaptive cuckoo search: Analysis and experimentation
dc.type	Journal Article
utslib.citation.volume	60
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0802 Computation Theory and Mathematics
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	2022-03-31T02:53:16Z
pubs.issue	100751
pubs.publication-status	Published
pubs.volume	60
utslib.citation.issue	100751

Abstract:

Cuckoo search (CS) algorithm, based on the brood parasitic behaviour of cuckoo species, since its inception, has proved its worth in various fields of research and can be considered as an efficient problem solver. Though CS is a very good algorithm, its performance degrades as the problem complexity increases. So a new version namely self-adaptive CS (SACS) is proposed to improve its performance. The algorithm employs adaptive parameters and hence no parameter tuning is required to be done. Here proportional population reduction based on the fitness of current best and previous best solution is followed. Secondly, in order to improve the exploration and exploitation tendencies, the Gaussian sampling mechanism known as bare-bones variant along with division of population and generations is added. The concept of Weibull distributed probability switching is also added to increase the balance between the exploration and exploitation processes. Further, CS as an extension of differential evolution (DE), genetic algorithm (GA), stability analysis with respect to Von Neumann’s stability criteria are also presented. For performance evaluation, the SACS algorithm is tested on CEC 2017 benchmark problems and compared with respect to self-adaptive DE (SaDE), JADE, success-history based adaptive DE (SHADE), SHADE with linear population size reduction (LSHADE), mean-variance mapping optimization (MVMO), cuckoo version 1.0 (CV1.0), cuckoo version new (CVnew) and others. Experimental results and statistical analysis show that the proposed SACS algorithm performs better than SaDE, JADE, CV1.0, CVnew and CS whereas comparable with respect to LSHADE, SHADE, and MVMO. Convergence profiles further validate the results.

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http://hdl.handle.net/10453/155751