Moth-flame glowworm swarm optimisation

Dabiah Ahmed Alboaneen*, Huaglory Tianfield, Yan Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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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.
Original languageEnglish
Pages (from-to)305-326
Number of pages22
JournalMultiagent and Grid Systems
Issue number3
Publication statusPublished - 25 Oct 2019


  • glowworm swarm optimisation (GSO)
  • metaheuristic
  • hybrid metaheuristic algorithm
  • moth-flame optimisation (MFO)


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