关键词: 粒子群优化, 局部搜索能力, 早熟收敛, 动态随机搜索技术, 佳点集, 负梯度

Abstract: In order to overcome the problems of poor local search and premature convergence on Particle Swarm Optimization (PSO) algorithm, an improved particle swarm optimization approach based on Dynamic Random Search Technique (DRST) and good-point set was proposed in this paper. DRST was introduced to optimize the current best position of the swarm. On the other hand, reinitialization with a good-point set manner was employed for the swarm falling into premature convergence to go out of the local optimum. Linear search in the negative gradient direction was also applied to accelerate the optimization. In the end, an experiment was given and the results show that the improved algorithm has rapid convergence, great ability of preventing premature convergence and better performance than Standard Particle Swarm Optimization (SPSO) and Dissipative Particle Swarm Optimization (DPSO).

Key words: Particle Swarm Optimization (PSO), local search ability, premature convergence, Dynamic Random Search Technique (DRST), good-point set, negative gradient