A Critical Examination of Optimization Algorithmic Developments, Applications, and Limitations in Crow Swarm Optimization Search Theory

Abstract

The Crow Swarm Optimization algorithm (CrSO) is a highly effective metaheuristic optimization technique inspired by the collective behavior of crow flocks and their strategies for hiding food. Drawing from the natural strategies crows use to protect and retrieve their food, CrSO has proven highly effective in solving complex optimization problems. Its ability to balance exploration and exploitation has made it a popular choice in various fields, including science, engineering, and data analysis. This paper comprehensively explores CrSO, beginning with its biological inspirations and extending to its mathematical foundations and algorithm framework. Additionally, it evaluates the CrSO’s performance in real-world applications while critically examining its limitations to provide a balanced perspective on its strengths and areas for improvement. However, CrSO has limitations by critically examining some challenges, such as sensitivity to parameter settings, computational complexity in high-dimensional spaces, and potential convergence issues in multi-modal problems.