A survey of Beluga whale optimization and its variants: Statistical analysis, advances, and structural reviewing

Abstract

Optimization, as a fundamental pillar in engineering, computer science, economics, and many other fields, plays a decisive role in improving the performance of systems and achieving desired goals. Optimization problems involve many variables, various constraints, and nonlinear objective functions. Among the challenges of complex optimization problems is the extensive search space with local optima that prevents reaching the global optimal solution. Therefore, intelligent and collective methods are needed to solve problems, such as searching for large problem spaces and identifying near-optimal solutions. Metaheuristic algorithms are a successful method for solving complex optimization problems. Usually, metaheuristic algorithms, inspired by natural and social phenomena, try to find optimal or near-optimal solutions by using random searches and intelligent explorations in the problem space. Beluga Whale Optimization (BWO) is one of the metaheuristic algorithms for solving optimization problems that has attracted the attention of researchers in recent years. The BWO algorithm tries to optimize the search space and achieve optimal solutions by simulating the collective behavior of whales. A study and review of published articles on the BWO algorithm show that this algorithm has been used in various fields, including optimization of mathematical functions, engineering problems, and even problems related to artificial intelligence. In this article, the BWO algorithm is classified according to four categories (combination, improvement, variants, and optimization). An analysis of 151 papers shows that the BWO algorithm has the highest percentage (49%) in the improvement field. The combination, variants, and optimization fields comprise 12%, 7%, and 32%, respectively.