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2012 Multiobjective Differential Evolution Algorithm with Multiple Trial Vectors
Yuelin Gao, Junmei Liu
Abstr. Appl. Anal. 2012: 1-12 (2012). DOI: 10.1155/2012/172041

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.

Citation

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Yuelin Gao. Junmei Liu. "Multiobjective Differential Evolution Algorithm with Multiple Trial Vectors." Abstr. Appl. Anal. 2012 1 - 12, 2012. https://doi.org/10.1155/2012/172041

Information

Published: 2012
First available in Project Euclid: 28 March 2013

zbMATH: 1253.90205
MathSciNet: MR2970003
Digital Object Identifier: 10.1155/2012/172041

Rights: Copyright © 2012 Hindawi

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