关键词: 粒子群, 自适应驱散, 分阶段加速, 加速收敛

Abstract: In order to overcome the problem that the Particle Swarm Optimization（PSO） algorithm is easy to fall into local optimal result in premature convergence, Adaptive Dispersion Mechanism Based Particle Swarm Optimization（ADMPSO） algorithm is proposed, in which adaptive dispersion mechanism and evolutionary state estimate frame are introduced. In order to avoid falling into the local optimal solution, most improved algorithms usually adjust the value of global optimum and individual optimum. However, this strategy will reduce the speed of convergence. Hence, this paper proposes a new adaptive dispersion mechanism which does not interrupt the optimization procedure until the swarms fall into the local optimal solution based on PSO. The frame of adaptive dispersion mechanism not only improves the quality of the optimal resulting, but also increases the speed of convergence. Empirical studies on 30D problems of 12 popular Benchmark Functions（BFs） have been carried out to evaluate the performances of ADMPSO algorithm. The experimental results show that the comprehensive effect of ADMPSO is the best compared with GPSO, CLPSO and DMS-PSO-HS. ADMPSO can effectively avoid trapping in the local optimum, find the better optimum steadily and keep a certain convergence speed at the same time.

Key words: particle swarm, adaptive dispersion, accelerate in different phases, accelerating convergence