Volume 8 Number 2 (Feb. 2013)
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JCP 2013 Vol.8(2): 294-301 ISSN: 1796-203X
doi: 10.4304/jcp.8.2.294-301

An Adaptive Multiobjective Differential Evolution Algorithm

Fangqing Gu, Hai-lin Liu
School of Applied Mathematics Guangdong University of Technology Guangzhou, China

Abstract—The mutation strategy and control parameter have a significant influence on the performance of differential evolution. An local and global mutation operator based subregion and external set strategies are proposed in this paper. They use the idea of direct simplex method of mathematical programming. It is advantageous to search the better solutions in the search space. The local mutation operator is applied to improve the local search performance of the algorithm and accelerate convergence speed. The global mutation operator is used to exploit a wider area and jump out of the local optima. An adaptive strategy for assigning mutation strategies and control parameters is proposed in this paper. The more successful is a mutation strategy and control parameter setting in the previous search, the more chance it will be used in the further search. Moreover, a novel crossover operator based subregion and external set strategy also is introduced. In order to demonstrate the performance of the proposed algorithm, it is compared with the MOEA/D-DE and the hybrid-NSGA-II-DE. The result indicates that the proposed algorithm is efficient.

Index Terms—differential evolution, multiobjective optimization, mutation strategy, adaptive strategy

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Cite: Fangqing Gu, Hai-lin Liu, " An Adaptive Multiobjective Differential Evolution Algorithm," Journal of Computers vol. 8, no. 2, pp. 294-301, 2013.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Monthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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