Advances in Spotted Hyena Optimizer: A Comprehensive Survey

Metaheuristic algorithms are widely used in various fields of optimization engineering. These algorithms have become popular because of their ability to explore and exploit solutions in various problem areas. The Spotted Hyena Optimizer (SHO) algorithm is a metaheuristic algorithm inspired by the life of spotted hyenas, introduced by Dhiman and Kumar (2017) to solve continuous optimization problems. Various studies have been performed based on changes in the SHO algorithm to solve various problems due to its effectiveness and success in solving continuous problems. This paper aims to comprehensively survey the application of the SHO algorithm in solving various optimization problems. In this paper, SHO algorithms are categorized based on hybridization, improvement, SHO variants, and optimization problems. This study invites researchers and developers of meta-heuristic algorithms to employ the SHO algorithm for solving diverse problems since it is a simple and robust algorithm for solving intricate and NP-hard problems. Based on the studies, it was concluded that the SHO algorithm had been used more in optimization problems. The purpose of optimization problems is to find optimal solutions and finding global points in the problem environment. Also, the SHO algorithm establishes a good trade-off between the exploration and extraction stages. Based on the done studies and investigations, properties and factors of the SHO algorithm are better than another meta-heuristic algorithms, which has increased its adaptability and flexibility in different fields.

Automated summary

Metaheuristic algorithms, particularly the Spotted Hyena Optimizer (SHO) algorithm, have shown great effectiveness and success in solving continuous optimization problems. This algorithm, inspired by the life of spotted hyenas, strikes a good balance between exploration and exploitation stages, making it suitable for solving intricate and NP-hard problems across various fields. Its adaptability and flexibility have been demonstrated to surpass those of other metaheuristic algorithms in different studies and investigations.