Enhancing Differential Evolution with Commensal Learning and Uniform Local Search

作     者：PENG Hu WU Zhijian DENG Changshou 

作者机构：State Key Laboratory of Software Engineering School of Computer Wuhan University School of Information Science and Technology Jiujiang University 

出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))

年 卷 期：2017年第26卷第4期

页      面：725-733页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(No.61364025) the Science and Technology Plan Projects of Jiangxi Provincial Education Department(No.GJJ13729) the Science and Technology Program of Nantong(No.BK2014057) 

主　　题：Differential evolution Commensal learn ing Uniform local search Global optimization 

摘      要：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 *** 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 CEC2013 test suite and comparison with the state-of-the-art DE variants indicate that the CUDE is very competitive.