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An Ensemble Differential Evolution for Numerical Optimization

Author

Listed:
  • Xiaobing Yu

    (Research Center for Prospering Jiangsu Province with Talents, Nanjing University of Information Science & Technology, Nanjing 210044, P. R. China;
    Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, P. R. China;
    China Institute for Manufacture Developing, Nanjing University of Information Science & Technology, Nanjing 210044, P. R. China;
    School of Economics and Management, 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;
    Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, 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)

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.

Suggested Citation

  • Xiaobing Yu & Xuming Wang & Jie Cao & Mei Cai, 2015. "An Ensemble Differential Evolution for Numerical Optimization," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 14(04), pages 915-942.
    • Handle: RePEc:wsi:ijitdm:v:14:y:2015:i:04:n:s0219622015500145
    DOI: 10.1142/S0219622015500145
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