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Nature-inspired approach: An enhanced moth swarm algorithm for global optimization

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  • Luo, Qifang
  • Yang, Xiao
  • Zhou, Yongquan

Abstract

The moth swarm algorithm (MSA) is a recent swarm intelligence optimization algorithm, but its convergence precision and ability can be limited in some applications. To enhance the MSA’s exploration abilities, an enhanced MSA called the elite opposition-based MSA (EOMSA) is proposed. For the EOMSA, an elite opposition-based strategy is used to enhance the diversity of the population and its exploration ability. The EOMSA was validated using 23 benchmark functions and three structure engineering design problems. The results show that the EOMSA can find a more accurate solution than other population-based algorithms, and it also has a fast convergence speed and high degree of stability.

Suggested Citation

  • Luo, Qifang & Yang, Xiao & Zhou, Yongquan, 2019. "Nature-inspired approach: An enhanced moth swarm algorithm for global optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 159(C), pages 57-92.
    • Handle: RePEc:eee:matcom:v:159:y:2019:i:c:p:57-92
    DOI: 10.1016/j.matcom.2018.10.011
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    References listed on IDEAS

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    1. Socha, Krzysztof & Dorigo, Marco, 2008. "Ant colony optimization for continuous domains," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1155-1173, March.
    2. Garg, Harish, 2016. "A hybrid PSO-GA algorithm for constrained optimization problems," Applied Mathematics and Computation, Elsevier, vol. 274(C), pages 292-305.
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    Cited by:

    1. Yan, Zheping & Zhang, Jinzhong & Zeng, Jia & Tang, Jialing, 2021. "Nature-inspired approach: An enhanced whale optimization algorithm for global optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 17-46.
    2. Gheisariha, Elmira & Tavana, Madjid & Jolai, Fariborz & Rabiee, Meysam, 2021. "A simulation–optimization model for solving flexible flow shop scheduling problems with rework and transportation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 180(C), pages 152-178.
    3. Hu, Gang & Du, Bo & Li, Huinan & Wang, Xupeng, 2022. "Quadratic interpolation boosted black widow spider-inspired optimization algorithm with wavelet mutation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 200(C), pages 428-467.
    4. Kang, Helei & Liu, Renyun & Yao, Yifei & Yu, Fanhua, 2023. "Improved Harris hawks optimization for non-convex function optimization and design optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 204(C), pages 619-639.

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