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Dynamic Niches-Based Hybrid Breeding Optimization Algorithm for Solving Multi-Modal Optimization Problem

Author

Listed:
  • Ting Cai

    (School of Computer Science, Hubei University of Technology, Wuhan 430068, China)

  • Ziteng Qiao

    (School of Computer Science, Hubei University of Technology, Wuhan 430068, China)

  • Zhiwei Ye

    (School of Computer Science, Hubei University of Technology, Wuhan 430068, China)

  • Hu Pan

    (School of Computer Science, Hubei University of Technology, Wuhan 430068, China)

  • Mingwei Wang

    (School of Computer Science, Hubei University of Technology, Wuhan 430068, China)

  • Wen Zhou

    (School of Computer Science, Hubei University of Technology, Wuhan 430068, China)

  • Qiyi He

    (School of Computer Science, Hubei University of Technology, Wuhan 430068, China)

  • Peng Zhang

    (Wuhan Fiberhome Technical Services Co., Ltd., Wuhan 430205, China)

  • Wanfang Bai

    (Xining Big Data Service Administration, Xining 810000, China)

Abstract

Some problems exist in classical optimization algorithms to solve multi-modal optimization problems and other complex systems. A Dynamic Niches-based Improved Hybrid Breeding Optimization (DNIHBO) algorithm is proposed to address the multi-modal optimization problem in the paper. By dynamically adjusting the niche scale, it effectively addresses the issue of niche parameter sensitivity. The structure of the algorithm includes three distinct groups: maintainer, restorer, and sterile lines for updating operations. However, the maintainer individuals often stagnate, leading to the risk of the local optima. To overcome this, neighborhood search and elite mutation strategies are incorporated, enhancing the balance between exploration and exploitation. To further improve individual utilization within niches, a niche restart strategy is introduced, ensuring sustained population diversity. The efficacy of DNIHBO is validated through simulations on 16 multi-modal test functions, followed by comparative analyses with various multi-modal optimization algorithms. The results convincingly demonstrate that DNIHBO not only effectively locates multiple global optima but also consistently outperforms other algorithms on test functions. These findings underscore the superiority of DNIHBO as a high-performing solution for multi-modal optimization.

Suggested Citation

  • Ting Cai & Ziteng Qiao & Zhiwei Ye & Hu Pan & Mingwei Wang & Wen Zhou & Qiyi He & Peng Zhang & Wanfang Bai, 2024. "Dynamic Niches-Based Hybrid Breeding Optimization Algorithm for Solving Multi-Modal Optimization Problem," Mathematics, MDPI, vol. 12(17), pages 1-25, September.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:17:p:2779-:d:1473933
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    References listed on IDEAS

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    1. Md. Jakirul Islam & Xiaodong Li & Kalyanmoy Deb, 2022. "A speciation-based bilevel niching method for multimodal truss design problems," Journal of Combinatorial Optimization, Springer, vol. 44(1), pages 172-206, August.
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