基于种群关系的多种群粒子群协同优化算法

传统粒子群优化算法容易陷入局部最优解,搜索效率不高,针对此问题,提出了一种基于种群关系和斥力因子的多种群粒子群优化算法SRB-PSO (Swarm-Relation-Based PSO).根据当前搜索结果定义种群之间统治、对等和被统治3种关系,通过引入斥力因子来保证种群间搜索的多样性,并通过统治和被统治关系提高算法的搜索效率,从而在改善算法的全局搜索性能的同时提高解的质量.将算法与其他几种主流粒子群优化改进算法在标准测试集上进行对比,实验结果证明了SRB-PSO算法能较好地保持粒子多样性,全局搜索能力强,在解决多峰函数时的性能优于其他几种主流粒子群优化改进算法.

Multi-Swarm Particle Swarm Optimization Based on Population Relation

Traditional Particle Swarm Optimization (PSO) is likely to converge to local optima when applied to multimodal problems, with low search efficiency. In this study, a novel multi-swarm PSO algorithm based on swarm relations and repulsion factors is proposed, called Swarm-Relation-Based PSO (SRB-PSO). Three swarm relations, including dominance, equivalence, and weakness, are defined according to the search results. The search diversity is guaranteed by introducing repulsion factors among equivalent populations and the search efficiency is increased by dominance and weakness relations. Thus, the global search ability of the algorithm is enhanced and the solution quality is improved. The new algorithm and several other versions of PSO are compared on a set of benchmark functions. The results show that the algorithm proposed in this study can well maintain the particle diversity and has outstanding global search ability. The proposed algorithm outperforms the other algorithms when solving multimodal problems.