Multiobjective Differential Evolution Algorithm with Multiple Trial Vectors

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 indi...

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Bibliographic Details
Published in:Abstract and Applied Analysis 2012-01, Vol.2012 (2012), p.916-927
Main Authors: Gao, Yuelin, Liu, Junmei
Format: Article
Language:English
Subjects:
Algorithms
Convergence
Evolution
Heuristic
Mathematical analysis
Operators
Optimization
Pareto optimality
Pareto optimum
Studies
Vectors (mathematics)
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Summary: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.
ISSN:1085-3375
1687-0409
DOI:10.1155/2012/172041