Affiliations: [a] Computer Science Department, College of Science and Humanities, Imam Abdulrahman Bin Faisal University, Jubail, Saudi Arabia | [b] Department of Computing, School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow, UK
Correspondence:
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Corresponding author: Dabiah Ahmed Alboaneen, Computer Science Department, College of Science and Humanities, Imam Abdulrahman Bin Faisal University, Jubail, Saudi Arabia. E-mail: [email protected].
Abstract: One of the drawbacks of glowworm swarm optimisation (GSO) is its premature convergence, which leaves it often ineffective for solving complex practical problems. This paper proposes a new hybrid metaheuristic algorithm, that is, moth-flame glowworm swarm optimisation (MFGSO). The main idea of the hybrid algorithm is to combine the exploration ability in moth-flame optimisation (MFO) with the exploitation ability in GSO. Performance evaluations are conducted on benchmarking test functions in comparison with the basic GSO and other metaheuristic algorithms. The results show that MFGSO outperforms the basic GSO and other metaheuristic algorithms on most test functions in terms of local optima avoidance and convergence speed.
