IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i8p1311-d794120.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/8/1311/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/8/1311/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Yongjing Li & Wenhui Pei & Qi Zhang, 2022. "Improved Whale Optimization Algorithm Based on Hybrid Strategy and Its Application in Location Selection for Electric Vehicle Charging Stations," Energies, MDPI, vol. 15(19), pages 1-25, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:8:p:1311-:d:794120. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.