White Shark Optimizer and its Applications: A Systematic Review

White Shark Optimizer (WSO) is an optimization algorithm inspired by nature-based methods that simulates and models the behavior of great white sharks. Inspired by the hunting methods and survival instincts of great white sharks, this algorithm is an intelligent problem-solving tool that relies primarily on their senses of hearing and smell. Great white sharks exhibit behaviors similar to those of predators, using their keen senses of smell and hearing to search for optimal solutions to complex optimization problems. This algorithm has attracted the attention of scientists, researchers, and engineers. This is because its ability to balance exploration (searching for new areas) and exploitation (optimizing known solutions) is similar to what white sharks do in the vast ocean when precisely targeting their target. Due to its flexibility, robustness, and performance, it is a tool for addressing difficult challenges in fields such as robotics, machine learning, image processing, scheduling, and power systems. Moreover, given its complex mechanism in some specific problem environments, a wide range of alternatives has been proposed to improve convergence speed, search efficiency, and robustness. This survey discusses the theoretical foundations and algorithmic structure of the WSO algorithm, examines its various modifications, and focuses on its use in diverse fields. It also examines its limitations in complex search spaces, highlighting potential drawbacks and areas for improvement. Finally, it offers perspectives on future research paths that may facilitate the development of this algorithm to address new optimization problems.