一种基于均值的云自适应粒子群算法

A Cloud Adaptive Particle Swarm Optimization Algorithm Based on Mean

本文基于云理论把粒子群分为三个种群,用云方法修改粒子群算法中惯性权重,同时修改速度更新公式中"认知部分"和"社会部分",引入"均值"的概念,提出了一种基于均值的云自适应粒子群算法。该方法的最大优点是克服了粒子群算法在迭代后期,当一些粒子的个体极值对应的适应度值与全局极值对应的适应度值相差明显时,不能收敛到最优解的缺点。数值实验结果表明,该算法经过较少的迭代次数,就能找到最优解,且平均运算时间减少,降低了算法的平均时间代价。

Based on the cloud adaptive theory,the particle swarm optimization algorithm is improved and the particle swarm is divided into three populations.It modifies the inertia weight using a cloud method,and meanwhile modifies the ＂social＂ and ＂cognitive＂ sections,and introduces the notion of mean,and proposes an improved cloud adaptive theory particle swarm optimization algorithm named MCAPSO.The greatest advantage of the method is that the algorithm in the later iteration,when the different value between an individual optimal to some particle corresponding of the fitness value and a global optimal corresponding to the fitness value is significant,overcomes the shortcoming that the algorithm does not benefit from converges to the optimal solution.Numerical experience shows that,MCAPSO runs less iteration to find the optimal solution,and the average time is lower.The average time cost is reduced accordingly.