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Information Technology Journal
  Year: 2013 | Volume: 12 | Issue: 9 | Page No.: 1796-1803
DOI: 10.3923/itj.2013.1796.1803
 
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Dual-system Cooperative Coevolutionary Differential Evolution Algorithm for Solving Nonseparable Function Optimization
Feng-Zhe Cui, Lei Wang, Zhi-Zheng Xu, Xiu-Kun Wang and Hong-Fei Teng

Abstract: In recent years, researches on high-dimensional nonseparable function optimization have made progress. Approaches based on Potter’s Cooperative Coevolutionary (CC) framework have achieved better results and aroused a great attention. However, the computational results are still unsatisfying for most Benchmark functions. Therefore, this study develops a dual-system (population) cooperative coevolutionary differential evolution (DCCDE) algorithm based on dual-system Evolutionary Algorithm (EA). This algorithm adopts a variable static grouping pattern and a improved Differential Evolution (DE) algorithm combined with simple crossover (SPX) local search strategy and modifies the migration pattern of the sub-individuals (not subpopulations) among the subsystems (subgroups of variables) in the dual-system. The test results of 20 Benchmark functions (including 17 nonseparable functions, dimension D = 1000) show that the proposed algorithm is better than other algorithms in computational accuracy.
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How to cite this article:

Feng-Zhe Cui, Lei Wang, Zhi-Zheng Xu, Xiu-Kun Wang and Hong-Fei Teng, 2013. Dual-system Cooperative Coevolutionary Differential Evolution Algorithm for Solving Nonseparable Function Optimization. Information Technology Journal, 12: 1796-1803.

DOI: 10.3923/itj.2013.1796.1803

URL: https://scialert.net/abstract/?doi=itj.2013.1796.1803
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