粒子群优化算法研究进展

Survey of Particle Swarm Optimization Algorithm

粒子群优化(PSO)算法作为一种仿生进化算法,是受到自然界生物群体行为机制的启发而提出的.本文首先介绍 PSO 算法的基本原理和工作机制.然后着重就 PSO 算法的理论和应用研究现状进行综述,包括 PSO 算法的改进、PSO 算法的参数设置、PSO 算法的收敛性、PSO 算法与其它算法的融合以及 PSO 算法在优化领域的典型应用,并进一步分析它们的研究重点和发展方向.最后是关于 PSO 算法面临的问题和研究展望,提出 PSO 算法研究中值得探讨的一些课题.

The particle swarm optimization （PSO） algorithm is an evolutionary algorithm that simulates the mechanism of biological swarm social behavior . The models of bird flocking and swarm actions are firstly introduced, and the fundamental characteristics and the working mechanisms of PSO algorithm are also analyzed . Then the recent progress in theory of PSO algorithm is reviewed, which are related to the improvement of PSO algorithm, the parameter selection in PSO algorithm, the convergence features of PSO algorithm, and the merging mechanism to other meta-heuristic optimization algorithms. In addition, several typical application areas of PSO algorithm are surveyed respectively, which include continuous function optimization, neural network training, optimization of power system and optimization in electromagnetics. Finally, some suggestions on future trends and existing problems related to PSO algorithm are discussed and concluded.