Title :
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
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;
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
DOI :
10.1109/ICICISYS.2009.5357850
