关键词: 粒子群优化, 逻辑自映射, 局部收敛, 稳定性, 群体智能

Abstract: Particle Swarm Optimization（PSO） is a stochastic global optimization evolutionary algorithm. In this paper, a novel Chaos Particle Swarm Optimization algorithm（CPSO） is proposed in order to overcome the poor stability and the disadvantage of easily getting into the local optimum of the Standard Particle Swarm Optimization（SPSO）. On the one hand, the uniform particles are produced by logical self-map function so as to improve the quality of the initial solutions and enhance the stability. On the other hand, two sets of velocity and position strategies are employed, that is to say, the special velocity-position is used for the global particles, while the general velocity-position is used for the rest particles in the swarm so as to prevent the particles from plunging into the local optimum. The CPSO proposed in this paper is applied to four benchmark functions and the experimental results show that CPSO can improve the performance of searching global optimum efficiently and own higher stability.

Key words: Particle Swarm Optimization（PSO）, logical self-map, local convergence, stability, swarm intelligence