一种自适应交替的粒子群差分进化优化算法

A self-adaptive alternating optimization algorithm based on particle swarm optimization and differential evolution

为了克服粒子群算法易陷入早熟收敛的缺点及提高差分进化算法的搜索能力,提出了一种自适应交替的粒子群差分进化算法.该算法采用自适应的概率交替使用PSO和DE,通过对6个基准函数的测试,说明本文提出的算法是一种收敛速度快、求解精度高的全局优化算法.

To overcome particle swarm optimization algorithm′s premature convergence and improve the search ability of differential evolution,a hybrid optimization algorithm PSO-DE was proposed.This algorithm alternately used PSO and DE by a self-adaptive probability.Six benchmark functions are used in the test,and the experimental results show that PSO-DE is a powerful global optimization algorithm with rapid converence speed and higher accuracy.