一种基于动态边界的粒子群优化算法

摘要

Standard particle swarm optimization presented in 2007 (namely,PSO-2007)inclines towards stagnation phenomena in the later stage of evolution,which leads to premature convergence.Therefore,a PSO based on dynamic boundary(namely,DBPSO) is proposed in this paper.According to the movement characteristics of particles at stagnation stage,DBPSO introduces a strategy of boundary adjusting in PSO-2007.By tracking the distribution of the particles' locations,DBPSO adjusts the boundary of search space dynamically,which could guide the particles to more promising region.This strategy helps PSO-2007 decrease premature convergence and improve convergence precision.The results of experiments of four typical functions show that DBPSO are feasible and effective.%2007年提出的标准粒子群优化算法(PO-2007)在进化的后期容易出现停滞现象而导致早熟收敛,为此本文提出了一种基于动态边界的粒子群优化算法(DBPSO).该算法根据停滞期粒子运动的特点,将边界动态调整策略引入到PSO-2007中,通过跟踪粒子飞行位置的分布动态调整搜索空间的边界,引导粒子在更有效的区域内进行搜索,从而减轻早熟收敛,提高收敛精度.典型测试函数的求解实验结果表明DBPSO是可行而有效的.