ABFIA: A hybrid algorithm based on artificial bee colony and Fibonacci indicator algorithm

Etminaniesfahani, A; Gu, H; Salehipour, A

ABFIA: A hybrid algorithm based on artificial bee colony and Fibonacci indicator algorithm

Etminaniesfahani, A Gu, H

Salehipour, A

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Journal of Computational Science, 2022, 61, pp. 1-13
Issue Date:: 2022-05-01

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Field	Value	Language
dc.contributor.author	Etminaniesfahani, A
dc.contributor.author	Gu, H https://orcid.org/0000-0003-2035-2583
dc.contributor.author	Salehipour, A
dc.date.accessioned	2022-12-05T00:55:49Z
dc.date.available	2022-12-05T00:55:49Z
dc.date.issued	2022-05-01
dc.identifier.citation	Journal of Computational Science, 2022, 61, pp. 1-13
dc.identifier.issn	1877-7503
dc.identifier.issn	1877-7511
dc.identifier.uri	http://hdl.handle.net/10453/164097
dc.description.abstract	The artificial bee colony (ABC) is a metaheuristic optimization algorithm known for its simplicity, flexibility, and efficiency. The algorithm, however, suffers from slow convergence due to a lack of a powerful local search capability. The Fibonacci indicator algorithm (FIA), on the other hand, is a recently proposed derivative-free metaheuristic that incorporates a powerful local search mechanism based on the line search method. This paper proposes hybridizing the artificial bee colony with the Fibonacci indicator algorithm to achieve strong exploration and highly efficient exploitation capabilities. We show that the hybrid algorithm is better than ABC and FIA and delivers superior outcomes for various optimization functions widely used in the literature, including 20 scalable basic and ten complex CEC2019 test functions.
dc.language	English
dc.publisher	Elsevier
dc.relation.ispartof	Journal of Computational Science
dc.relation.isbasedon	10.1016/j.jocs.2022.101651
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0802 Computation Theory and Mathematics, 0806 Information Systems
dc.title	ABFIA: A hybrid algorithm based on artificial bee colony and Fibonacci indicator algorithm
dc.type	Journal Article
utslib.citation.volume	61
utslib.for	0802 Computation Theory and Mathematics
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building/School of Built Environment
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
utslib.copyright.embargo	2024-05-01T00:00:00+1000Z
dc.date.updated	2022-12-05T00:55:48Z
pubs.publication-status	Published
pubs.volume	61

Abstract:

The artificial bee colony (ABC) is a metaheuristic optimization algorithm known for its simplicity, flexibility, and efficiency. The algorithm, however, suffers from slow convergence due to a lack of a powerful local search capability. The Fibonacci indicator algorithm (FIA), on the other hand, is a recently proposed derivative-free metaheuristic that incorporates a powerful local search mechanism based on the line search method. This paper proposes hybridizing the artificial bee colony with the Fibonacci indicator algorithm to achieve strong exploration and highly efficient exploitation capabilities. We show that the hybrid algorithm is better than ABC and FIA and delivers superior outcomes for various optimization functions widely used in the literature, including 20 scalable basic and ten complex CEC2019 test functions.

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http://hdl.handle.net/10453/164097