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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
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa54989
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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 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.
Keywords: Firefly algorithm, Artificial bee colony, Multi-strategy, Hybrid algorithm, Global optimization
College: Faculty of Science and Engineering
Issue: 1
Start Page: 810
End Page: 821

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