基于混沌思想的粒子群优化算法  

Modified Particle Swarm Optimization Algorithm Based on Chaos

作　　者：岳兴汉[1] 薛云灿[1] 蔡亮[1] 

机构地区：[1]河海大学计算机及信息工程学院,江苏常州213022

出　　处：《河海大学常州分校学报》2007年第4期5-8,共4页Journal of Hohai University Changzhou

基　　金：国家高技术研究发展计划资助项目(2006AA050304)

摘　　要：为克服粒子群优化算法容易陷入局部最优的缺点,根据混沌运动的随机性、遍历性特点,提出一种基于混沌思想的粒子群优化算法(CPSO).该算法利用种群适应度方差进行早熟收敛判断,实现对进化过程的监视,当发现种群陷入局部最优时,对种群进行混沌初始化,帮助种群摆脱局部最优点.对4种典型测试函数的仿真结果表明,改进算法明显减少了种群陷入局部最优的可能性,其全局寻优能力明显强于标准粒子群优化算法.Particle swarm optimization algorithm is a swarm intelligence algorithm, which is easily trapped in local optima. To overcome this shortcoming, a modified particle swarm optimization algorithm based on Chaos is proposed, which make use of the stochastic property and ergodicity of chaotic search. In the modified algorithm, the population is re-initialized when the fitness variance is smaller than a set number, which means the population is trapped in local optima. The experimental results on the typical benchmark functions demonstrate that the modified PSO is superior to original PSO in breaking away from local optima and global searching.

关 键 词：粒子群优化算法 混沌运动 局部最优 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构] TP18[自动化与计算机技术—计算机科学与技术]