一种精英反向学习的粒子群优化算法

Elite Opposition-Based Particle Swarm Optimization

为解决传统粒子群优化算法易出现早熟的不足,提出了精英反向学习策略,引入精英粒子,采用反向学习生成其反向解,扩大搜索区域的范围,可增强算法的全局勘探能力.同时,为避免最优粒子陷入局部最优而导致整个群体出现搜索停滞,提出了差分演化变异策略,采用差分演化算法搜索最优粒子的邻域空间,可增强算法的局部开采能力.在14个测试函数上将本文算法与多种知名的PSO算法进行对比,实验结果表明本文算法在解的精度与收敛速度上更优.

Traditional particle swarm optimization（PSO）algorithm tends to suffer from premature convergence;we proposed an elite opposition-based learning strategy in which elite particles are introduced to generate their opposite solutions by opposition-based learning.This mechanism can expand the search area and is helpful to enhance the global explorative ability of PSO.Meanwhile,a differential evolutionary mutation strategy is presented to avoid the best particle being trapped into local optima,since this may cause search stagnation of the whole swarm.This strategy adopts differential evolution algorithm to search for the neighborhoods of the global best particle and is helpful to enhance the exploitation ability of PSO.We compared our algorithm with some state-of-the-art PSOs on 14 benchmarks,the results show that our algorithm obtains better solution accuracy and quicker convergence speed.