"粒子群优化算法：设计、原理与参数设置综述"

​Particle swarm optimization is a novel heuristic algorithm based on swarm intelligence that aims to find the global optimum in complex search spaces through competition and cooperation among particles. It is known for its easy-to-understand nature, ease of implementation, and strong global search capabilities, making it one of the fastest-growing intelligent optimization algorithms in science and engineering fields. This paper introduces the basic principles of particle swarm optimization algorithm and analyzes its characteristics. A comprehensive review of the principles, characteristics, parameter settings, and applications of the algorithm is provided, with a focus on using single-factor variance analysis to examine the impact of inertia weight and acceleration factor settings on the basic performance of the algorithm, along with recommendations for empirical parameter settings. Finally, suggestions and research directions for future studies are proposed. Keywords: particle swarm optimization algorithm, parameters, variance analysis, optimal solution.