DocumentCode
3265582
Title
A Modified Differential Evolution Multi-objective Optimization Method
Author
Zhang, L.B. ; Xu, X.L. ; Sun, C.T. ; Zhou, C.G.
Author_Institution
Coll. of Comput. Sci. & Technol., Jilin Univ., Changchun, China
Volume
1
fYear
2009
fDate
6-7 June 2009
Firstpage
511
Lastpage
514
Abstract
Differential evolution (DE) is population based, direct search, global optimization new algorithm. DE is simple and efficient, so solving multi-objective optimization problems (MOP) using DE has become a new hot research topic. It was found in the research process that there are deteriorations in the process of solving MOP based on DE. Due to deterioration occurs, thus the convergence can no longer be guaranteed for the algorithm, and the efficiency is reduced for solving. This paper proposed resolve methods correspond to these two deteriorations. Numerical experiments show the effectiveness of the proposed algorithm.
Keywords
Pareto optimisation; evolutionary computation; Pareto dominance; modified differential evolution method; multiobjective optimization problem; Computational intelligence; Computer science; Convergence; Degradation; Educational institutions; Evolutionary computation; Genetic mutations; Optimization methods; Pareto optimization; Sun; Pareto dominance; differential evolution; mulit-objective optimization;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence and Natural Computing, 2009. CINC '09. International Conference on
Conference_Location
Wuhan
Print_ISBN
978-0-7695-3645-3
Type
conf
DOI
10.1109/CINC.2009.145
Filename
5231073
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