关键词: 综合信息策略, 压缩因子, 粒子群算法

Abstract: The diversity of swarm will be impaired in late period of evolution for a swarm intelligent algorithm and the convergence of each individual element is enhanced, so the major disadvantage of particle swarm optimizer is vulnerable to be trapped in the local optima. This paper proposes a new variant particle swarm optimizer which combines constrict factor and comprehensive informed strategy. The constrict factor can balance the global and local models, and comprehensive informed strategy can efficiently enhance the diversity of all particles. By comparing the standard particle swarm optimizer, adaptive particle swarm optimizer and particle swarm optimizer based on constrict factor on 7 test functions with accuracy level, success rate and convergence velocity, the results show that the new algorithm can obtain a higher accurate level and faster convergence velocity.

Key words: comprehensive informed, constrict factor, particle swarm optimizer