微粒群优化算法(Particle Swarm Optimization,PSO算法)源于鸟群和鱼群群体运

动行为的研究，是一种新的群体智能优化算法，是演化计算领域中的一个新的分支。它

的主要特点是原理简单、参数少、收敛速度较快，所需领域知识少。该算法的出现引起

了学者们极大的关注，已在函数优化、神经网络训练、组合优化、机器人路径规划等领

域获得了广泛应用，并取得了较好的效果。尽管粒子群优化算法发展近十年，但无论是

理论分析还是实践应用都尚未成熟，有大量的问题值得研究。

-Particle swarm optimization (Particle Swarm Optimization, PSO algorithm) from groups of birds and fish movement behavior, is a new swarm intelligence algorithm, in the field of evolutionary computation is a new branch. Its main feature is simple in principle, few parameters, convergence is faster, less domain knowledge required. The algorithm brought the scholars are of great concern, has been in function optimization, neural network training, combinatorial optimization, robot path planning has been widely used applications, and achieved good results. Despite the development of particle swarm optimization nearly a decade, but both theory and practice applications are not yet mature, a large number of issues worth studying.