Wild horse optimizer: a new meta-heuristic algorithm for solving engineering optimization problems

• Published in: Engineering with computers Vol. 38; no. Suppl 4; pp. 3025 - 3056
• Main Authors: Naruei, Iraj, Keynia, Farshid
• Format: Journal Article
• Language: English
• Published: London Springer London 01.10.2022; Springer Nature B.V
• Subjects: Algorithms; Animals; CAE) and Design; Calculus of Variations and Optimal Control; Optimization; Classical Mechanics; Computer Science; Computer-Aided Engineering (CAD; Control; Design optimization; Heuristic methods; Horses; Math. Applications in Chemistry; Mathematical and Computational Engineering; Optimization algorithms; Original Article; Source code; Systems Theory; Optimization techniques; Meta-heuristic algorithm; Horse algorithm; Wild horse optimizer
• ISSN: 0177-0667; 1435-5663
• DOI: 10.1007/s00366-021-01438-z

Nowadays, the design of optimization algorithms is very popular to solve problems in various scientific fields. The optimization algorithms usually inspired by the natural behaviour of an agent, which can be humans, animals, plants, or a physical or chemical agent. Most of the algorithms proposed in the last decade inspired by animal behaviour. In this article, we present a new optimizer algorithm called the wild horse optimizer (WHO), which is inspired by the social life behaviour of wild horses. Horses usually live in groups that include a stallion and several mares and foals. Horses exhibit many behaviours, such as grazing, chasing, dominating, leading, and mating. A fascinating behaviour that distinguishes horses from other animals is the decency of horses. Horse decency behaviour is such that the foals of the horse leave the group before reaching puberty and join other groups. This departure is to prevent the father from mating with the daughter or siblings. The main inspiration for the proposed algorithm is the decency behaviour of the horse. The proposed algorithm was tested on several sets of test functions such as CEC2017 and CEC2019 and compared with popular and new optimization methods. The results showed that the proposed algorithm presented very competitive results compared to other algorithms. The source code is currently available for public from: https://www.mathworks.com/matlabcentral/fileexchange/90787-wild-horse-optimizer .