A comprehensive survey of golden jacal optimization and its applications

Abstract

In recent decades, there has been an increasing interest from the research community in various scientific and engineering fields, including robotic control, signal processing, image processing, feature selection, classification, clustering, and other issues. Many optimization problems are inherently complicated and complex. They cannot be solved by traditional optimization methods, such as mathematical programming, because most conventional optimization methods focus on evaluating first derivatives. On the other hand, metaheuristic algorithms have high ability and adaptability in finding near-optimal solutions in a reasonable time for different optimization problems due to parallel search and balance between exploration and exploitation. This study discusses the basic principles and mechanisms of the GJO algorithm and its challenges. This review aims to provide valuable insights into the potential of the GJO algorithm for real-world and scientific optimization tasks. In this paper, a complete review of the Golden Jackal Optimization (GJO) algorithm for various optimization problems is done. The GJO algorithm is one of the metaheuristic algorithms invented in 2022 and inspired by the life of natural jackals. This paper's complete classification of GJO in hybrid, improved, binary, multi-objective, and optimization problems is done. The analysis shows that the percentage of studies conducted in the four fields of hybrid, improved variants of GJO (binary, multi-objective), and optimization are 11 %, 44 %, 9 %, and 36 %, respectively. Studies have shown that this algorithm performs well in real-world challenges. GJO is a powerful tool for solving scientific and engineering problems flexibly.