An Ensemble Differential Evolution for Numerical Optimization

Xiaobing Yu (), Xuming Wang, Jie Cao and Mei Cai
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Xiaobing Yu: Research Center for Prospering Jiangsu Province with Talents, Nanjing University of Information Science & Technology, Nanjing 210044, P. R. China;
Xuming Wang: School of Electronic and Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, P. R. China
Jie Cao: Research Center for Prospering Jiangsu Province with Talents, Nanjing University of Information Science & Technology, Nanjing 210044, P. R. China;
Mei Cai: School of Economics and Management, Nanjing University of Information Science & Technology, Nanjing 210044, P. R. China

International Journal of Information Technology & Decision Making (IJITDM), 2015, vol. 14, issue 04, 915-942

Abstract: The success of differential evolution (DE) in solving a specific problem crucially depends on appropriately choosing generation strategies and control parameter values. The mutation strategies of DE are classified into two groups: DE/rand/k without best solution and DE/best/k with best solution. The proposed algorithm utilizes two mutation strategies. The first one is from DE/rand/k and the second one is from DE/best/k. The proposed algorithm uses two control parameter settings. It randomly combines them to generate trial vectors. The novel mechanism improves the convergence rate of DE and maintains diversity of the population. The performance of the proposed algorithm is extensively evaluated on all the CEC2005 test functions and compares favorably with the several DE variants.

Keywords: Differential evolution; evolutionary algorithm; numerical optimization; mutation strategy (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1142/S0219622015500145

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