摘要
粒子群优化(PSO)算法是一类有效的随机全局优化技术,适用于求解连续优化问题。它利用一个粒子群搜索解空间,通过粒子间的相互作用发现复杂搜索空间中的最优区域。本文介绍了基本的PSO算法,使用3类代表性的标准测试函数对粒子群算法进行了实验分析,并进一步讨论了PSO算法的寻优性能,提出了PSO求解连续优化问题的性能分析策略。
Particle swarm algorithm is a stochastic global optimization technique and it is fit for function optimization. The particle swarm algorithm find optimal regions of complex search spaces through the interaction of individuals in a population of particles. In this paper, classical particle swarm optimization algorithm is introduced. Furthermore results of experiments on three benchmark functions are shown and they demonstrate the efficiency of PSO in different ways.
出处
《微计算机应用》
2008年第10期87-91,共5页
Microcomputer Applications
关键词
粒子群
函数优化
群集智能
particle swarm, function optimization, swarm intelligence
