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Multi-Strategy Enhanced Crested Porcupine Optimizer: CAPCPO

Author

Listed:
  • Haijun Liu

    (School of Emergency Management, Institute of Disaster Prevention, Langfang 065201, China
    These authors contributed equally to this work.)

  • Rui Zhou

    (School of Emergency Management, Institute of Disaster Prevention, Langfang 065201, China
    These authors contributed equally to this work.)

  • Xiaoyong Zhong

    (School of Emergency Management, Institute of Disaster Prevention, Langfang 065201, China
    National Institute of Emergency Management, Party School of the Central Committee of C.P.C (National Academy of Governance), Beijing 100089, China)

  • Yuan Yao

    (Institute of Mineral Resources Research, China Metallurgical Geology Bureau, Beijing 101300, China)

  • Weifeng Shan

    (Institute of Intelligent Emergency Information Processing, Institute of Disaster Prevention, Langfang 065201, China)

  • Jing Yuan

    (School of Information Engineering, Institute of Disaster Prevention, Langfang 065201, China)

  • Jian Xiao

    (School of Emergency Management, Institute of Disaster Prevention, Langfang 065201, China)

  • Yan Ma

    (School of Emergency Management, Institute of Disaster Prevention, Langfang 065201, China)

  • Kunpeng Zhang

    (College of Computer Science and Technology, Jilin University, Changchun 130012, China)

  • Zhibin Wang

    (Gientech Digital Technology Group Co., Ltd., Beijing 100192, China
    C4, Dongsheng Science and Technology Park, No. 66 Xixiaokou Road, Haidian District, Beijing 100192, China)

Abstract

Metaheuristic algorithms are widely used in engineering problems due to their high efficiency and simplicity. However, engineering challenges often involve multiple control variables, which present significant obstacles for metaheuristic algorithms. The Crested Porcupine Optimizer (CPO) is a metaheuristic algorithm designed to address engineering problems, but it faces issues such as falling into a local optimum. To address these limitations, this article proposes three new strategies: composite Cauchy mutation strategy, adaptive dynamic adjustment strategy, and population mutation strategy. The three proposed strategies are then introduced into CPO to enhance its optimization capabilities. On three well-known test suites, the improved CPO (CAPCPO) outperforms 11 metaheuristic algorithms. Finally, comparative experiments on seven real-world engineering optimization problems demonstrate the advantages and potential of CAPCPO in solving complex problems. The multifaceted experimental results indicate that CAPCPO consistently achieves superior solutions in most cases.

Suggested Citation

  • Haijun Liu & Rui Zhou & Xiaoyong Zhong & Yuan Yao & Weifeng Shan & Jing Yuan & Jian Xiao & Yan Ma & Kunpeng Zhang & Zhibin Wang, 2024. "Multi-Strategy Enhanced Crested Porcupine Optimizer: CAPCPO," Mathematics, MDPI, vol. 12(19), pages 1-41, October.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:19:p:3080-:d:1490412
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    References listed on IDEAS

    as
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    3. Walaa N. Ismail & Hessah A. Alsalamah, 2023. "Efficient Harris Hawk Optimization (HHO)-Based Framework for Accurate Skin Cancer Prediction," Mathematics, MDPI, vol. 11(16), pages 1-26, August.
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