随机局部搜索扰动的粒子群优化算法

Particle swarm optimization with stochastic local search

在粒子群优化(PSO)算法中,gBest粒子的行为对算法的收敛性能有较大的影响。提出一种新的改进粒子群优化算法——SLS-PSO算法。该算法以基本PSO算法为框架,融合随机局部搜索算法(SLS)对进化中的gBest粒子进行局部寻优计算,以改善PSO算法在进化中特别是进化后期的收敛性能。通过典型测试函数的计算表明,该算法在收敛速度和精度上都有不同程度的改善。

In Particle Swarm Optimization （PSO） algorithm, the acting of gBest particle during evolutionary process is important for attaining convergence. A new improved PSO algorithm called SLS-PSO was proposed in this paper. Based on the structure of basic PSO algorithm, the proposed algorithm searched local optimal solutions to gBest particle by adopting Stochastic Local Search （SLS） algorithm to improve the convergence performance during evolutionary process of PSO algorithm. Four well-designed test problems were used to evaluate the proposed algorithm. Compared with the basic PSO algorithm, the proposed algorithm shows its effectiveness and efficiency.