PENG Hu, WU Zhijian, DENG Changshou, “Enhancing Differential Evolution with Commensal Learning and Uniform Local Search,” Chinese Journal of Electronics, vol. 26, no. 4, pp. 725-733, 2017, doi: 10.1049/cje.2016.11.010
Citation: PENG Hu, WU Zhijian, DENG Changshou, “Enhancing Differential Evolution with Commensal Learning and Uniform Local Search,” Chinese Journal of Electronics, vol. 26, no. 4, pp. 725-733, 2017, doi: 10.1049/cje.2016.11.010

Enhancing Differential Evolution with Commensal Learning and Uniform Local Search

doi: 10.1049/cje.2016.11.010
Funds:  This work is supported by the National Natural Science Foundation of China (No.61364025), the Science and Technology Plan Projects of Jiangxi Provincial Education Department (No.GJJ13729), and the Science and Technology Program of Nantong (No.BK2014057).
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  • Corresponding author: WU Zhijian (corresponding author) works for the State Key Laboratory of Software Engineering, School of Computer, Wuhan University, Wuhan, China. He received the B.S. degree in mathematics from Jiangxi University, China, in 1983, the M.S. degree from the Department of mathematics, Wuhan University, in 1988, and the Ph.D. degree from the School of Computing, Wuhan University, Wuhan, China, in 2004. Now he is a full professor with the State Key Laboratory of Software Engineering, Wuhan University, Wuhan, China. His current research interests include evolutionary computation, intelligent computing, parallel computing, and intelligent information processing. (Email: zhijianwu@whu.edu.cn)
  • Received Date: 2015-04-10
  • Rev Recd Date: 2015-08-20
  • Publish Date: 2017-07-10
  • Differential evolution (DE) is a popular and powerful evolutionary algorithm for global optimization problems. However, the combination of mutation strategies and parameter settings of DE is problem dependent and choosing the suitable one is a challenge work and timeconsuming. In addition, the deficiency in local exploitation also has a significant influence on the performance of DE. In order to solve these problems, a DE variant with Commensal learning and uniform local search (CUDE) has been proposed in this paper. In CUDE, commensal learning is proposed to adaptively select optimal mutation strategy and parameter setting simultaneously under the same criteria. Moreover, uniform local search enhances exploitation ability. Comprehensive experiment results on all the CEC 2013 test suite and comparison with the state-of-the-art DE variants indicate that the CUDE is very competitive.
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