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A Hybrid Firefly and Multi-Strategy Artificial Bee Colony Algorithm
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Xinwei Cao
International Journal of Computational Intelligence Systems, Volume: 13, Issue: 1, Pages: 810 - 821
Swansea University Author:
Shuai Li
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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...
| Published in: | International Journal of Computational Intelligence Systems |
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| ISSN: | 1875-6891 1875-6883 |
| Published: |
Atlantis Press
2020
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa54989 |
| 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. |
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| 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 |