Improved moth search algorithm with mutation operator for numerical optimization problems

Sanaa A. A. Ghaleb, Mumtazimah Mohamad, Waheed Ali Hussein Mohammed Ghanem, Arifah Che Alhadi, Abdullah B. Nasser, Hanan Aldowah


The moth search algorithm (MSA) is a meta-heuristic optimization technique inspired by moth behavior, has shown remarkable efficacy in solving optimization challenges. However, its poor exploration capability results in an imbalance between exploitation and exploration. To address this issue, this research introduces a new mutation operator to enhance exploration by increasing population diversity. The proposed enhanced moth search algorithm (EMSA) aims to expedite convergence and improve overall robustness by exploring new solutions more effectively. Evaluation on ten benchmark functions demonstrates EMSA's superior exploration capabilities, efficiently tackling optimization problems and yielding more optimal solutions within the search space. Compared to conventional MSA and other established algorithms, EMSA delivers well-balanced results, showcasing its effectiveness in optimizing the search space. In the future, the EMSA could potentially find applications in addressing real-world engineering optimization challenges.


Meta-heuristic; Optimization algorithm; Exploitation; Exploration; Moth search algorithm; Mutation operator

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The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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