文档介绍：第 35 卷第 11 期
2009 年 11 月
自动化学报
ACTA AUTOMATICA SINICA
Vol. 35, No. 11
November, 2009
基于信息素机制的粒子群优化算法的设计与实现
吕强 1
刘士荣 1
邱雪娜 2
摘
要
提出了一种基于信息素机制的粒子群优化(Particle swarm optimization based on pheromone mechanism, PSO-
PM) 算法. 主要是借鉴了蚁群优化算法的信息素共享机制, 并引入到粒子群优化算法中, 设计了粒子行为的三条简单规则: 信
息留存规则、信息获取和融合规则以及粒子演化规则, 从而实现了群体信息的充分分享, 相应地改善了算法的寻优能力. 采用
基准函数对 PSO-PM 算法进行测试, 并与几种不同类型的改进优化算法进行对比, 数值实验结果验证了 PSO-PM 算法的有
效性.
关键词
信息素机制, 粒子群优化, 蚁群优化, 演化规则, 概率分布
中图分类号
TP18
Design and Realization of Particle Swarm Optimization
Based on Pheromone Mechanism
LV Qiang1
LIU Shi-Rong1
QIU Xue-Na2
Abstract
A particle swarm optimization based on pheromone mechanism (PSO-PM) is proposed. Through introducing
the idea of pheromone-shared mechanism used by ant colony optimization to the particle swarm optimization, and designing
three simple behavior rules including reserving information rule, requiring and syncretizing information rule, and evolving
rule, population information can be fully shared. Therefore, the algorithm s ability of searching optimum value is improved.
Compared with other optimization algorithms for the benchmark functions in the experiment, the obtained results have
demonstrated the effectiveness of proposed algorithm.
Key words
Pheromone mechanism, particle swarm optimization (PSO), ant colony optimization, evolvement rule,
probability distribu