一种基于云模型的改进型粒子群算法

Improved Particle Swarm Optimization Algorithm Based on Cloud Model Theory

针对经典粒子群算法在函数优化中易陷入局部最优和早熟收敛等缺点,结合云模型在定性与定量之间相互转换的优良特性,提出一种基于云模型的改进型粒子群算法。其思想是通过反向学习机制初始化种群,再通过正态云算子求解粒子群中的全局最优个体和自身最优个体周围的更优值,最后利用混沌理论对个别粒子进行变异来跳出局部最优解。典型复杂函数测试表明,该算法能有效找出全局最优解,特别适宜于多峰值函数寻优。

Particle swarm optimization algorithm for optimization in function easily falls into local optimal solution and the premature quickly converges of such shortcomings .Combined with the excellent characteristics of cloud model transfor-mation between qualitative and quantitative ,an improved particle swarm optimization algorithm based on cloud model theory is proposed .The idea is to initialize the population through reverse learning mechanism ,to better solve value around the global best individual and its optimal individual in PSO by the normal cloud particle operator .Finally ,the individual particles are mutation to jump out of local optimal solution by using the theory of chaos .The simulation results show that the pro-posed algorithm has fine capability of finding global optimum ,especially multi peak function .