计算机工程与应用2017，Vol.53Issue(4)：25-32,38,9.DOI:10.3778/j.issn.1002-8331.1607-0382
最优粒子增强探索粒子群算法
Optimal particle enhanced exploration particle swarm optimization
摘要
Abstract
A modified Particle Swarm Optimization(PSO), Optimal particle Enhanced Exploration Particle Swarm Opti-mization(OEEPSO), is presented with the aim of solving the problem of premature convergence, slow searching speed and low solution accuracy. A new strategy is used to update the velocity and the position of optimal particle. It divides D-dimensions of optimal particle into several groups with each group contains two dimensions. Each group is updated by the position which has the best fitness in four different directions. This strategy expands the searching space around optimal particle and let optimal particle move to the position which is nearer to the optimum solution. This strategy accelerates the searching speed and reaches higher solution accuracy. OEEPSO also proposes a new strategy to avoid local optima. It utilizes the fitness of optimal particle to determine the velocity, so that particle swarm can escape from local optima effec-tively and find a better optimal particle. OEEPSO has been tested on 6 benchmark functions. Results show that OEEPSO has better performance than many other PSO algorithms in terms of convergence speed, global optimality and solution accuracy.关键词
粒子群算法/最优粒子/增强探索/维度划分/适应值Key words
particle swarm optimization/optimal particle/enhanced exploration/dimension division/fitness分类
信息技术与安全科学引用本文复制引用
唐祎玲,江顺亮,叶发茂,许庆勇,葛芸,徐少平..最优粒子增强探索粒子群算法[J].计算机工程与应用,2017,53(4):25-32,38,9.基金项目
国家自然科学基金(No.2012F020503,No.2011D010702). （No.2012F020503,No.2011D010702）
