基于混沌云模型的粒子群优化算法

摘要

针对传统粒子群优化(PSO)算法寻优精度不高和易陷入局部收敛区域的缺点,引入混沌算法和云模型算法对PSO算法的进化机制进行优化,提出混沌云模型粒子群优化(CCMPSO)算法.在算法处于收敛状态时将粒子分为优秀粒子和普通粒子,应用云模型算法和优秀粒子对收敛区域局部求精,发掘全局最优位置；应用混沌算法和普通粒子对收敛区域以外空间进行全局寻优,探索全局最优位置.应用特征根法对CCMPSO算法的收敛性进行分析,并通过仿真实验证明,CCMPSO算法的寻优性能优于其他常用PSO算法.%To deal with the problems of low accuracy and local convergence in conventional Particle Swarm Optimization (PSO) algorithm, the chaos algorithm and cloud model algorithm were introduced into the evolutionary process of PSO algorithm and the chaos cloud model particle swarm optimization (CCMPSO) algorithm was proposed. The particles were divided into excellent particles and normal particles when CCMPSO was in convergent status. To search the global optimum location, the cloud model algorithm as well as excellent particles was applied to local refinement in convergent area, meanwhile chaos algorithm and normal particles were used to global optimization in the outside space of convergent area. The convergence of CCMPSO was analyzed by eigenvalue method. The simulation results prove the CCMPSO has better optimization performance than other main PSO algorithms.