An Improved Wild Horse Optimizer for Solving Optimization Problems

Rong Zheng, Abdelazim G. Hussien, He-Ming Jia, Laith Abualigah, Shuang Wang and Di Wu
Additional contact information
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
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
Shuang Wang: School of Information Engineering, Sanming University, Sanming 365004, China
Di Wu: School of Education and Music, Sanming University, Sanming 365004, China

Mathematics, 2022, vol. 10, issue 8, 1-30

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.

Keywords: wild horse optimizer; metaheuristic; optimization; exploration and exploitation; engineering design problem (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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