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.

元启发式算法广泛应用于优化工程的各个领域。这些算法之所以流行，是因为它们能够探索和利用各种问题领域的解决方案。Spotted Hyena Optimizer (SHO) 算法是一种元启发式算法，其灵感来自于斑点鬣狗的生活，由 Dhiman 和 Kumar (2017) 引入，用于解决连续优化问题。由于 SHO 算法在解决连续问题方面的有效性和成功，已经根据 SHO 算法的变化进行了各种研究，以解决各种问题。本文旨在全面考察SHO算法在解决各种优化问题中的应用。在本文中，SHO 算法根据杂交、改进、SHO 变体和优化问题进行分类。本研究邀请元启发式算法的研究人员和开发人员使用 SHO 算法来解决各种问题，因为它是一种用于解决复杂和 NP 难题的简单而强大的算法。基于研究，得出的结论是，SHO 算法在优化问题中得到了更多的应用。优化问题的目的是寻找最优解，在问题环境中寻找全局点。此外，SHO 算法在探索和提取阶段之间建立了良好的权衡。基于所做的研究和调查，SHO算法的特性和因素都优于其他元启发式算法，增加了其在不同领域的适应性和灵活性。