Improving Cuckoo Search: Incorporating Changes for CEC 2017 and CEC 2020 Benchmark Problems

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

Improving Cuckoo Search: Incorporating Changes for CEC 2017 and CEC 2020 Benchmark Problems

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

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings, 2020, 00, pp. 1-7
Issue Date:: 2020-07-01

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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	2020-07-19
dc.date.accessioned	2021-02-02T04:51:09Z
dc.date.available	2021-02-02T04:51:09Z
dc.date.issued	2020-07-01
dc.identifier.citation	2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings, 2020, 00, pp. 1-7
dc.identifier.isbn	9781728169293
dc.identifier.uri	http://hdl.handle.net/10453/145732
dc.description.abstract	© 2020 IEEE. Cuckoo search (CS) is a highly competitive single objective optimization technique. The algorithm has been widely applied in various diverse application domains and has been found to be efficient in solving various real-life problems. In the present work, we have proposed a new enhanced version of CS algorithm and tested its performance on recently proposed CEC 2017 and CEC 2020 benchmark test problems. The proposed algorithm has been named as CSsin and it employs four major modifications, i) new techniques for global and local search are devised, ii) dual search strategy is followed to enhance exploration and exploitation properties of CS algorithm, iii) a linearly decreasing switch probability has been used to add a balance between local and global search, and iv) linearly decreasing population size is used to reduce the computational burden. Apart from these modifications, the division of iterations has been employed as a further modification. The CSsin algorithm has been tested on IEEE CEC 2017 and CEC 2020 benchmark test problems having various dimension sizes and a comparative study has been performed with respect to stateof-the-art optimization algorithms for single objective bound constraint optimization problems. The results of statistical significance test affirm the competitiveness of the proposed algorithm with respect to state-of-the-art techniques.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings
dc.relation.ispartof	2020 IEEE Congress on Evolutionary Computation (CEC)
dc.relation.isbasedon	10.1109/CEC48606.2020.9185684
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Improving Cuckoo Search: Incorporating Changes for CEC 2017 and CEC 2020 Benchmark Problems
dc.type	Conference Proceeding
utslib.citation.volume	00
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2022-07-01T00:00:00+1000Z
dc.date.updated	2021-02-02T04:51:02Z
pubs.finish-date	2020-07-24
pubs.publication-status	Published
pubs.start-date	2020-07-19
pubs.volume	00

Abstract:

© 2020 IEEE. Cuckoo search (CS) is a highly competitive single objective optimization technique. The algorithm has been widely applied in various diverse application domains and has been found to be efficient in solving various real-life problems. In the present work, we have proposed a new enhanced version of CS algorithm and tested its performance on recently proposed CEC 2017 and CEC 2020 benchmark test problems. The proposed algorithm has been named as CSsin and it employs four major modifications, i) new techniques for global and local search are devised, ii) dual search strategy is followed to enhance exploration and exploitation properties of CS algorithm, iii) a linearly decreasing switch probability has been used to add a balance between local and global search, and iv) linearly decreasing population size is used to reduce the computational burden. Apart from these modifications, the division of iterations has been employed as a further modification. The CSsin algorithm has been tested on IEEE CEC 2017 and CEC 2020 benchmark test problems having various dimension sizes and a comparative study has been performed with respect to stateof-the-art optimization algorithms for single objective bound constraint optimization problems. The results of statistical significance test affirm the competitiveness of the proposed algorithm with respect to state-of-the-art techniques.

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http://hdl.handle.net/10453/145732