Dynamic Population-based particle swarm optimization combined with crossover operator

摘要：

Particle swarm optimization (PSO) is a new swarm intelligent optimization technique. Although it maintains a fast convergent speed, it is still easy trapped into a local optimum when dealing with high-dimensional numerical problems. To overcome this shortcoming, in this paper, a new variant of PSO is designed hybrid with a dynamic population strategy and crossover operator. Simulation results show this new variant is superior to two other previous modifications in high-dimensional multimodel benchmarks.

关键词： Logistic model crossovere particle swarm optimization population size population growth Fitness-Distance-Ratio

作者: Yanjiang Miao Zhihua Cui Jianchao Zeng

作者单位: Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Taiyuan, Shanxi, PR.China, 030024

会议类型: 国际会议

会议名称: 2009 Ninth International Conference on Hybrid Intelligent Systems(第九届混合智能系统国际会议 HIS 2009)

会议地点: 沈阳

会议语种:英文

页码: 1-6

在线出版日期: 2009-08-12（万方平台首次上网日期，不代表论文的发表时间）