粒子群优化算法分析

粒子群优化算法是一种基于群体智能的随机全局优化技术，尽管其原理简单易、于实现且功能强大，但目前研究人员还没有对它的工作原理做出足够的解释．将群体优化过程看成一个动态系统的演变，采用线性离散时间系统的分析方法对算法的收敛性进行了分析，导出了简化PSO算法的收敛条件．考虑到参数是影响算法性能和效率的关键因素，利用标准测试函数对算法的参数选择进行了详细的分析，并给出一些指导性原则．

Parameter analysis of particle swarm optimization algorithm

Particle swarm optimization(PSO) algorithm is a swarm-intelligence-based stochastic global optimization technique originating from artificial life and evolutionary computation.Though the algorithm has been shown to perform well,the researchers haven't adequately explained how it works.In this paper,the swarm optimization was considered as the evolution of a dynamical system.The convergence property of PSO was analyzed by using the linear discrete time system method.The convergence conditions for simplified PSO algorithm were derived.Since the parameters were very important to the performance and efficiency of PSO,the selection of parameters was systematically discussed through several benchmark functions,and some instructional suggestions were also presented.