基于聚类的多子群粒子群优化算法

A Multi Sub-population Particle Swarm Optimizer Based on Clustering

在粒子群优化算法基础上,提出了基于聚类的多子群粒子群优化算法。该算法在每次迭代过程中首先通过聚类方法把粒子群体分成若干个子群体,然后粒子群中的粒子根据其个体极值和“子群”中的最优粒子更新自己的速度和位置值。这种处理增加了粒子之间的信息交换,利用了更多粒子在迭代过程中的信息,使算法的收敛性能更好。仿真结果表明,该算法的性能优于粒子群优化算法。

On the basis of the particle swarm optimizer, A cluster-based particle swarm optimizer is proposed. In the proposed algorithm, the current particles is first divided into multi sub-population by clustering. Then, the current particles is updated by the personal best particle and gobal best particles in the sub-populations. The proposed algorithm exchanged and uses more particles＇ information, thus improves convergence performance. The experiment results demonstrate that the proposed algorithm is superior to original particle swarm optimization algorithm.