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MFO-SFR: An Enhanced Moth-Flame Optimization Algorithm Using an Effective Stagnation Finding and Replacing Strategy

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  • Mohammad H. Nadimi-Shahraki

    (Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad 8514143131, Iran
    Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad 8514143131, Iran)

  • Hoda Zamani

    (Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad 8514143131, Iran
    Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad 8514143131, Iran)

  • Ali Fatahi

    (Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad 8514143131, Iran
    Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad 8514143131, Iran)

  • Seyedali Mirjalili

    (Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane 4006, Australia
    Yonsei Frontier Lab, Yonsei University, Seoul 03722, Republic of Korea)

Abstract

Moth-flame optimization (MFO) is a prominent problem solver with a simple structure that is widely used to solve different optimization problems. However, MFO and its variants inherently suffer from poor population diversity, leading to premature convergence to local optima and losses in the quality of its solutions. To overcome these limitations, an enhanced moth-flame optimization algorithm named MFO-SFR was developed to solve global optimization problems. The MFO-SFR algorithm introduces an effective stagnation finding and replacing (SFR) strategy to effectively maintain population diversity throughout the optimization process. The SFR strategy can find stagnant solutions using a distance-based technique and replaces them with a selected solution from the archive constructed from the previous solutions. The effectiveness of the proposed MFO-SFR algorithm was extensively assessed in 30 and 50 dimensions using the CEC 2018 benchmark functions, which simulated unimodal, multimodal, hybrid, and composition problems. Then, the obtained results were compared with two sets of competitors. In the first comparative set, the MFO algorithm and its well-known variants, specifically LMFO, WCMFO, CMFO, ODSFMFO, SMFO, and WMFO, were considered. Five state-of-the-art metaheuristic algorithms, including PSO, KH, GWO, CSA, and HOA, were considered in the second comparative set. The results were then statistically analyzed through the Friedman test. Ultimately, the capacity of the proposed algorithm to solve mechanical engineering problems was evaluated with two problems from the latest CEC 2020 test-suite. The experimental results and statistical analysis confirmed that the proposed MFO-SFR algorithm was superior to the MFO variants and state-of-the-art metaheuristic algorithms for solving complex global optimization problems, with 91.38% effectiveness.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:862-:d:1061480
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    References listed on IDEAS

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    1. Mohammad H. Nadimi-Shahraki & Ali Fatahi & Hoda Zamani & Seyedali Mirjalili, 2022. "Binary Approaches of Quantum-Based Avian Navigation Optimizer to Select Effective Features from High-Dimensional Medical Data," Mathematics, MDPI, vol. 10(15), pages 1-30, August.
    2. Jayashree Piri & Puspanjali Mohapatra & Biswaranjan Acharya & Farhad Soleimanian Gharehchopogh & Vassilis C. Gerogiannis & Andreas Kanavos & Stella Manika, 2022. "Feature Selection Using Artificial Gorilla Troop Optimization for Biomedical Data: A Case Analysis with COVID-19 Data," Mathematics, MDPI, vol. 10(15), pages 1-31, August.
    3. Fan Wang & Xiang Liao & Na Fang & Zhiqiang Jiang, 2022. "Optimal Scheduling of Regional Combined Heat and Power System Based on Improved MFO Algorithm," Energies, MDPI, vol. 15(9), pages 1-30, May.
    4. 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.
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