关键词: 粒子群优化算法, 优化, 群智能, 多向学习, 自适应

Abstract: Particle Swarm Optimization（PSO） is a new globe optimization algorithm based on swarm intelligent search. It is an algorithm for searching the global optimum in the complex space through cooperation and competition among the individuals in a population of particle. But the basic PSO has some demerits, such as relapsing into local extremum, slow convergence velocity and low convergence precision in the late evolutionary. This paper proposes an improved Particle Swarm Optimization algorithm（PSO）; the algorithm completes the optimization through following the personal best solution of each particle, the best solution of same dimensions of other stochastic particle and the global best value of the whole swarm on speed update, through judging by area boundary on position update. The experiment demonstrates that the proposed improved method is efficient and valid to solve the related problems, and avoid the premature convergence problem effectively, so it is suitable to be applied in the engineering.

Key words: Particle Swarm Optimization（PSO） algorithm, optimization, swarm intelligence, comprehensive learning, self-adaptive