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An effective improved differential evolution algorithm to solve constrained optimization problems

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Abstract

An effective extended differential evolution algorithm is proposed to deal with constrained optimization problems. The proposed algorithm adopts a new mechanism to cope with constrained problems by transforming the equality into inequality first. Then, two kinds of offspring generation approaches are applied to balance the diversity and the convergence speed of the population during evolution, and seven criteria are designed to compare feasible solution over infeasible solution. The performance of the novel algorithm is evaluated on a set of well-known constrained problems from CEC2006. The experimental results are quite competitive when comparing the proposed algorithm against state-of-the-art optimization algorithms.

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Acknowledgements

The authors would like to thank the associate editor and the anonymous reviewers for their very helpful suggestions. This study was funded by China Natural Science Foundation (Grant Numbers 71503134, 91546117, 71373131, 71171116), Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), Key Project of National Social and Scientific Fund Program (16ZDA047), Research Center for Prospering Jiangsu Province with Talents (Grant Number skrc201400-14), Natural Science Foundation of Higher Education of Jiangsu Province of China (16KJB120003), Philosophy and Social Sciences in Universities of Jiangsu (Grant Number 2016SJB630016), and Research Institute for History of Science and Technology (2017KJSKT005).

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Authors and Affiliations

  1. Research Institute for History of Science and Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Xiaobing Yu

  2. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Xiaobing Yu

  3. China Institute for Manufacture Developing, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Xiaobing Yu

  4. School of Economics and Management, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Xiaobing Yu, Yiqun Lu, Xiang Luo & Mei Cai

  5. School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Xuming Wang

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  3. Xuming Wang
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Communicated by V. Loia.

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Yu, X., Lu, Y., Wang, X. et al. An effective improved differential evolution algorithm to solve constrained optimization problems. Soft Comput 23, 2409–2427 (2019). https://doi.org/10.1007/s00500-017-2936-5

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