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Abstract and Applied Analysis
Volume 2012 (2012), Article ID 172041, 12 pages
http://dx.doi.org/10.1155/2012/172041
Research Article

Multiobjective Differential Evolution Algorithm with Multiple Trial Vectors

1Institute of Information and System Science, Beifang University of Nationalities, Yinchuan 750021, China
2Department of Mathematics, Yinchuan College, China University of Mining and Technology, Yinchuan 750011, China

Received 29 May 2012; Accepted 5 June 2012

Academic Editor: Yonghong Yao

Copyright © 2012 Yuelin Gao and Junmei Liu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents a multiobjective differential evolution algorithm with multiple trial vectors. For each individual in the population, three trial individuals are produced by the mutation operator. The offspring is produced by using the crossover operator on the three trial individuals. Good individuals are selected from the parent and the offspring and then are put in the intermediate population. Finally, the intermediate population is sorted according to the Pareto dominance relations and the crowding distance, and then the outstanding individuals are selected as the next evolutionary population. Comparing with the classical multiobjective optimization algorithm NSGA-II, the proposed algorithm has better convergence, and the obtained Pareto optimal solutions have better diversity.