期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 46, 期 1, 页码 219-232出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2015.2399478
关键词
Control parameter adaptation; differential evolution (DE) algorithm; mutation strategy adaptation; zoning evolution
类别
资金
- 973 project of China [2013CB733600]
- National Natural Science Foundation of China [21176073]
- Program for New Century Excellent Talents in University [NCET-09-0346]
- Fundamental Research Funds for the Central Universities
The performance of the differential evolution (DE) algorithm is significantly affected by the choice of mutation strategies and control parameters. Maintaining the search capability of various control parameter combinations throughout the entire evolution process is also a key issue. A self-adaptive DE algorithm with zoning evolution of control parameters and adaptive mutation strategies is proposed in this paper. In the proposed algorithm, the mutation strategies are automatically adjusted with population evolution, and the control parameters evolve in their own zoning to self-adapt and discover near optimal values autonomously. The proposed algorithm is compared with five state-of-the-art DE algorithm variants according to a set of benchmark test functions. Furthermore, seven nonparametric statistical tests are implemented to analyze the experimental results. The results indicate that the overall performance of the proposed algorithm is better than those of the five existing improved algorithms.
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