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Improving Wild Horse Optimizer: Integrating Multistrategy for Robust Performance across Multiple Engineering Problems and Evaluation Benchmarks

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  • Lei Chen

    (School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China)

  • Yikai Zhao

    (School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China)

  • Yunpeng Ma

    (School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China)

  • Bingjie Zhao

    (School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China)

  • Changzhou Feng

    (School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China)

Abstract

In recent years, optimization problems have received extensive attention from researchers, and metaheuristic algorithms have been proposed and applied to solve complex optimization problems. The wild horse optimizer (WHO) is a new metaheuristic algorithm based on the social behavior of wild horses. Compared with the popular metaheuristic algorithms, it has excellent performance in solving engineering problems. However, it still suffers from the problem of insufficient convergence accuracy and low exploration ability. This article presents an improved wild horse optimizer (I-WHO) with early warning and competition mechanisms to enhance the performance of the algorithm, which incorporates three strategies. First, the random operator is introduced to improve the adaptive parameters and the search accuracy of the algorithm. Second, an early warning strategy is proposed to improve the position update formula and increase the population diversity during grazing. Third, a competition selection mechanism is added, and the search agent position formula is updated to enhance the search accuracy of the multimodal search at the exploitation stage of the algorithm. In this article, 25 benchmark functions (Dim = 30, 60, 90, and 500) are tested, and the complexity of the I-WHO algorithm is analyzed. Meanwhile, it is compared with six popular metaheuristic algorithms, and it is verified by the Wilcoxon signed-rank test and four real-world engineering problems. The experimental results show that I-WHO has significantly improved search accuracy, showing preferable superiority and stability.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:18:p:3861-:d:1236690
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    References listed on IDEAS

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    1. Hashim, Fatma A. & Houssein, Essam H. & Hussain, Kashif & Mabrouk, Mai S. & Al-Atabany, Walid, 2022. "Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 192(C), pages 84-110.
    2. 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.
    3. Kang, Helei & Liu, Renyun & Yao, Yifei & Yu, Fanhua, 2023. "Improved Harris hawks optimization for non-convex function optimization and design optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 204(C), pages 619-639.
    4. Kaveh, Mehrdad & Mesgari, Mohammad Saadi & Saeidian, Bahram, 2023. "Orchard Algorithm (OA): A new meta-heuristic algorithm for solving discrete and continuous optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 208(C), pages 95-135.
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    Cited by:

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    2. Ahmed Saeed Abdelrazek Bayoumi & Ragab A. El-Sehiemy & Mahmoud Badawy & Mostafa Elhosseini & Mansourah Aljohani & Amlak Abaza, 2023. "Optimizing Multi-Layer Perovskite Solar Cell Dynamic Models with Hysteresis Consideration Using Artificial Rabbits Optimization," Mathematics, MDPI, vol. 11(24), pages 1-16, December.

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