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An Improved Wild Horse Optimizer for Solving Optimization Problems

Author

Listed:
  • Rong Zheng

    (School of Information Engineering, Sanming University, Sanming 365004, China)

  • Abdelazim G. Hussien

    (Department of Computer and Information Science, Linköping University, 581 83 Linköping, Sweden
    Faculty of Science, Fayoum University, Faiyum 63514, Egypt)

  • He-Ming Jia

    (School of Information Engineering, Sanming University, Sanming 365004, China)

  • Laith Abualigah

    (Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan
    School of Computer Science, Universiti Sains Malaysia, Gelugor 11800, Malaysia)

  • Shuang Wang

    (School of Information Engineering, Sanming University, Sanming 365004, China)

  • Di Wu

    (School of Education and Music, Sanming University, Sanming 365004, China)

Abstract

Wild horse optimizer (WHO) is a recently proposed metaheuristic algorithm that simulates the social behavior of wild horses in nature. Although WHO shows competitive performance compared to some algorithms, it suffers from low exploitation capability and stagnation in local optima. This paper presents an improved wild horse optimizer (IWHO), which incorporates three improvements to enhance optimizing capability. The main innovation of this paper is to put forward the random running strategy (RRS) and the competition for waterhole mechanism (CWHM). The random running strategy is employed to balance exploration and exploitation, and the competition for waterhole mechanism is proposed to boost exploitation behavior. Moreover, the dynamic inertia weight strategy (DIWS) is utilized to optimize the global solution. The proposed IWHO is evaluated using twenty-three classical benchmark functions, ten CEC 2021 test functions, and five real-world optimization problems. High-dimensional cases ( D = 200, 500, 1000) are also tested. Comparing nine well-known algorithms, the experimental results of test functions demonstrate that the IWHO is very competitive in terms of convergence speed, precision, accuracy, and stability. Further, the practical capability of the proposed method is verified by the results of engineering design problems.

Suggested Citation

  • Rong Zheng & Abdelazim G. Hussien & He-Ming Jia & Laith Abualigah & Shuang Wang & Di Wu, 2022. "An Improved Wild Horse Optimizer for Solving Optimization Problems," Mathematics, MDPI, vol. 10(8), pages 1-30, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:8:p:1311-:d:794120
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    References listed on IDEAS

    as
    1. Abdelazim G. Hussien & Diego Oliva & Essam H. Houssein & Angel A. Juan & Xu Yu, 2020. "Binary Whale Optimization Algorithm for Dimensionality Reduction," Mathematics, MDPI, vol. 8(10), pages 1-24, October.
    2. Gui-Ying Ning & Dun-Qian Cao & Manuel De la Sen, 2021. "Improved Whale Optimization Algorithm for Solving Constrained Optimization Problems," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-13, February.
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    Cited by:

    1. Lei Chen & Yikai Zhao & Yunpeng Ma & Bingjie Zhao & Changzhou Feng, 2023. "Improving Wild Horse Optimizer: Integrating Multistrategy for Robust Performance across Multiple Engineering Problems and Evaluation Benchmarks," Mathematics, MDPI, vol. 11(18), pages 1-35, September.
    2. Shuang Wang & Abdelazim G. Hussien & Heming Jia & Laith Abualigah & Rong Zheng, 2022. "Enhanced Remora Optimization Algorithm for Solving Constrained Engineering Optimization Problems," Mathematics, MDPI, vol. 10(10), pages 1-32, May.

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