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

Lei Chen, Yikai Zhao, Yunpeng Ma (), Bingjie Zhao and Changzhou Feng
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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

Mathematics, 2023, vol. 11, issue 18, 1-35

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.

Keywords: metaheuristic algorithm; wild horse optimizer; adaptive parameter; early warning strategy; competitive selection mechanism (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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