文献类型：期刊文献

英文题名：Self-Adaptive Differential Evolution Algorithm With Zoning Evolution of Control Parameters and Adaptive Mutation Strategies

作者：Fan, Qinqin[1];Yan, Xuefeng[1]

机构：[1]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份：2016

卷号：46

期号：1

起止页码：219

外文期刊名：IEEE TRANSACTIONS ON CYBERNETICS

收录：;EI(收录号：20151200667489);WOS:【SCI-EXPANDED(收录号:WOS:000367144300020)】；

基金：This work was supported in part by the 973 project of China under Grant 2013CB733600, in part by the National Natural Science Foundation of China under Grant 21176073, in part by the Program for New Century Excellent Talents in University under Grant NCET-09-0346, and in part by the Fundamental Research Funds for the Central Universities. This paper was recommended by Y. Jin.

语种：英文

外文关键词：Control parameter adaptation; differential evolution (DE) algorithm; mutation strategy adaptation; zoning evolution

摘要：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.