基于信息素机制的粒子群优化算法的设计与实现

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.

Design and Realization of Particle Swarm Optimization Based on Pheromone Mechanism

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.