A PARTICLE SWARM OPTIMIZATION ALGORITHM WITH CROSSOVER OPERATOR

摘要：

Particle swarm optimization (PSO) is a method for tackling optimization functions.However, it is easily trapped into the local optimization when solving high-dimension functions.To overcome this shortcoming, a modified particle swarm optimization is proposed in this paper.In the proposed method, a crossover step is added to the standard PSO.The crossover is taken between each particles individual best position.After the crossover, the fitness of the individual best position is compared with that of the two offspring, and the best one is taken as the new individual best position.The crossover can help the particles jump out of the local optimization by sharing the others information.The experiment on five benchmark functions shows that the modified PSO is more effective to find the global optimal solution than other methods.

关键词： Particle swarm optimization Swarm intelligence Crossover

作者: ZHI-FENG HAO ZHI-GANG WANG HAN HUANG

作者单位: School of Mathematical Sciences, South China University of Technology, Guangzhou 510640, China;Colle School of Mathematical Sciences, South China University of Technology, Guangzhou 510640, China College of Computer Science and Engineering, South China University of Technology, Guangzhou 510640,

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 1036-1040

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）