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
Link To Document :
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