Clustering-based Selection for Evolutionary Multi-objective Optimization
(0) In this study, a novel clustering-based selection strategy of nondominated individuals for evolutionary multi-objective optimization is proposed. The new strategy partitions the nondominated individuals in current Pareto front adaptively into desired clusters. Then one representative individual will be selected in each cluster for pruning nondominated individuals. In order to evaluate the validity of the new strategy, we apply it into one state of the art multi-objective evolutionary algorithm. The experimental results based on thirteen benchmark problems show that the new strategy improves the performance obviously in terms of breadth and uniformity of nondominated solutions.
Multi-objective optimization Evolutionary algorithm Nondominated individual Selection
Maoguo Gong Gang Cheng Licheng Jiao Chao Liu
Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China,Institute of Intelligent Information Processing,PO Box 224,Xidian University,Xian,710071,China
国际会议
上海
英文
255-259
2009-11-20（万方平台首次上网日期，不代表论文的发表时间）