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Dealing with multi-modality using synthesis of Moth-flame optimizer with sine cosine mechanisms

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  • Chen, Chengcheng
  • Wang, Xianchang
  • Yu, Helong
  • Wang, Mingjing
  • Chen, Huiling

Abstract

Evolutionary population-based methods have found their applications in dealing with many real-world simulation experiments and mathematical modelling problems. The Moth-flame optimization (MFO) algorithm is one of the swarm intelligence algorithms and it can be used with constrained and unknown search spaces. However, there are still some defects in its performance, such as low solution accuracy, slow convergence, and insufficient exploration capability. This study improves the basic MFO algorithm from the perspective of improving exploration capability and proposes a hybrid swarm-based algorithm called SMFO. The essential notion is to further explore and scan the feature space with taking advantages of the sine cosine strategy. We methodically investigated the efficacy, solutions, and optimization compensations of the developed SMFO using more than a few demonstrative benchmark tests, together with unimodal, multimodal, hybrid and composition tasks, and two widely applied engineering test problems. The simulations point towards this fact that the diversification and intensification inclinations of the original MFO and its convergence traits are fortunately upgraded. The findings and remarks show that the suggested SMFO is a favourable algorithm and it can show superior efficacy compared to other techniques.

Suggested Citation

  • Chen, Chengcheng & Wang, Xianchang & Yu, Helong & Wang, Mingjing & Chen, Huiling, 2021. "Dealing with multi-modality using synthesis of Moth-flame optimizer with sine cosine mechanisms," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 188(C), pages 291-318.
    • Handle: RePEc:eee:matcom:v:188:y:2021:i:c:p:291-318
    DOI: 10.1016/j.matcom.2021.04.006
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    References listed on IDEAS

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    1. Chen, Huiling & Wang, Mingjing & Zhao, Xuehua, 2020. "A multi-strategy enhanced sine cosine algorithm for global optimization and constrained practical engineering problems," Applied Mathematics and Computation, Elsevier, vol. 369(C).
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    Cited by:

    1. Yanhong Feng & Hongmei Wang & Zhaoquan Cai & Mingliang Li & Xi Li, 2023. "Hybrid Learning Moth Search Algorithm for Solving Multidimensional Knapsack Problems," Mathematics, MDPI, vol. 11(8), pages 1-28, April.
    2. Mohammad H. Nadimi-Shahraki & Hoda Zamani & Ali Fatahi & Seyedali Mirjalili, 2023. "MFO-SFR: An Enhanced Moth-Flame Optimization Algorithm Using an Effective Stagnation Finding and Replacing Strategy," Mathematics, MDPI, vol. 11(4), pages 1-28, February.
    3. Yu, Xiaobing & Wang, Haoyu & Lu, Yangchen, 2024. "An adaptive ranking moth flame optimizer for feature selection," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 219(C), pages 164-184.

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