Clustering-based selection for evolutionary multi-objective optimization

Author

Gong, Maoguo ; Jiao, Licheng ; Cheng, Gang ; Liu, Chao

Author_Institution

Key Lab. of Intell. Perception & Image Understanding of Minist. of Educ. of China, Xidian Univ., Xi´´an, China

Volume

1

fYear

2009

fDate

20-22 Nov. 2009

Firstpage

255

Lastpage

259

Abstract

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.

Keywords

Pareto optimisation; evolutionary computation; Pareto front; clustering-based selection strategy; evolutionary multiobjective optimization; multiobjective evolutionary algorithm; nondominated individuals; strategy partitions; Chaos; Design optimization; Evolutionary computation; Genetic algorithms; Information processing; Nearest neighbor searches; Particle swarm optimization; Sorting; Evolutionary algorithm; Multi-objective optimization; Nondominated individual; Selection;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-4244-4754-1

Electronic_ISBN

978-1-4244-4738-1

Type

conf

DOI

10.1109/ICICISYS.2009.5357850

Filename

5357850