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A Hybrid Firefly and Multi-Strategy Artificial Bee Colony Algorithm

Ivona Brajević, Predrag S. Stanimirović, Shuai Li Orcid Logo, Xinwei Cao

International Journal of Computational Intelligence Systems, Volume: 13, Issue: 1, Pages: 810 - 821

Swansea University Author: Shuai Li Orcid Logo

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DOI (Published version): 10.2991/ijcis.d.200612.001

Abstract

Many hard optimization problems have been efficiently solved by two notable swarm intelligence algorithms, artificial bee colony (ABC) and firefly algorithm (FA). In this paper, a collaborative hybrid algorithm based on firefly and multi-strategy artificial bee colony, abbreviated as FA-MABC, is pro...

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Published in: International Journal of Computational Intelligence Systems
ISSN: 1875-6891 1875-6883
Published: Atlantis Press 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa54989
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spelling 2020-09-16T16:29:22.9224527 v2 54989 2020-08-17 A Hybrid Firefly and Multi-Strategy Artificial Bee Colony Algorithm 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2020-08-17 ACEM Many hard optimization problems have been efficiently solved by two notable swarm intelligence algorithms, artificial bee colony (ABC) and firefly algorithm (FA). In this paper, a collaborative hybrid algorithm based on firefly and multi-strategy artificial bee colony, abbreviated as FA-MABC, is proposed for solving single-objective optimization problems. In the proposed algorithm, FA investigates the search space globally to locate favorable regions of convergence. A novel multi-strategy ABC is employed to perform local search. The proposed algorithm incorporates a diversity measure to help in the switch criteria. The FA-MABC is tested on 40 benchmark functions with diverse complexities. Comparative results with the basic FA, ABC and other recent state-of-the-art metaheuristic algorithms demonstrate the competitive performance of the FA-MABC. Journal Article International Journal of Computational Intelligence Systems 13 1 810 821 Atlantis Press 1875-6891 1875-6883 Firefly algorithm, Artificial bee colony, Multi-strategy, Hybrid algorithm, Global optimization 23 6 2020 2020-06-23 10.2991/ijcis.d.200612.001 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University 2020-09-16T16:29:22.9224527 2020-08-17T10:02:56.3193654 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Ivona Brajević 1 Predrag S. Stanimirović 2 Shuai Li 0000-0001-8316-5289 3 Xinwei Cao 4 54989__17943__92263a24f6b64e4fb5a8c9bb1f67e741.pdf 54989.pdf 2020-08-17T10:05:23.6042150 Output 2273353 application/pdf Version of Record true This is an open access article distributed under the CC BY-NC 4.0 license. true English http://creativecommons.org/licenses/by-nc/4.0/
title A Hybrid Firefly and Multi-Strategy Artificial Bee Colony Algorithm
spellingShingle A Hybrid Firefly and Multi-Strategy Artificial Bee Colony Algorithm
Shuai Li
title_short A Hybrid Firefly and Multi-Strategy Artificial Bee Colony Algorithm
title_full A Hybrid Firefly and Multi-Strategy Artificial Bee Colony Algorithm
title_fullStr A Hybrid Firefly and Multi-Strategy Artificial Bee Colony Algorithm
title_full_unstemmed A Hybrid Firefly and Multi-Strategy Artificial Bee Colony Algorithm
title_sort A Hybrid Firefly and Multi-Strategy Artificial Bee Colony Algorithm
author_id_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author Shuai Li
author2 Ivona Brajević
Predrag S. Stanimirović
Shuai Li
Xinwei Cao
format Journal article
container_title International Journal of Computational Intelligence Systems
container_volume 13
container_issue 1
container_start_page 810
publishDate 2020
institution Swansea University
issn 1875-6891
1875-6883
doi_str_mv 10.2991/ijcis.d.200612.001
publisher Atlantis Press
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
document_store_str 1
active_str 0
description Many hard optimization problems have been efficiently solved by two notable swarm intelligence algorithms, artificial bee colony (ABC) and firefly algorithm (FA). In this paper, a collaborative hybrid algorithm based on firefly and multi-strategy artificial bee colony, abbreviated as FA-MABC, is proposed for solving single-objective optimization problems. In the proposed algorithm, FA investigates the search space globally to locate favorable regions of convergence. A novel multi-strategy ABC is employed to perform local search. The proposed algorithm incorporates a diversity measure to help in the switch criteria. The FA-MABC is tested on 40 benchmark functions with diverse complexities. Comparative results with the basic FA, ABC and other recent state-of-the-art metaheuristic algorithms demonstrate the competitive performance of the FA-MABC.
published_date 2020-06-23T04:59:45Z
_version_ 1821380271803465728
score 11.04748

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