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An improved differential evolution algorithm and its application in optimization problem

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Abstract

The selection of the mutation strategy for differential evolution (DE) algorithm plays an important role in the optimization performance, such as exploration ability, convergence accuracy and convergence speed. To improve these performances, an improved differential evolution algorithm with neighborhood mutation operators and opposition-based learning, namely NBOLDE, is developed in this paper. In the proposed NBOLDE, the new evaluation parameters and weight factors are introduced into the neighborhood model to propose a new neighborhood strategy. On this basis, a new neighborhood mutation strategy based on DE/current-to-best/1, namely DE/neighbor-to-neighbor/1, is designed in order to replace large-scale global mutation by local neighborhood mutation with high search efficiency. Then, a generalized opposition-based learning is employed to optimize the initial population and select the better solution between the current solution and reverse solution in order to approximate global optimal solution, which can amend the convergence direction, accelerate convergence, improve efficiency, enhance the stability and avoid premature convergence. Finally, the proposed NBOLDE is compared with four state-of-the-art DE variants by 12 benchmark functions with low-dimension and high-dimension. The experiment results indicate that the proposed NBOLDE has a faster convergence speed, higher convergence accuracy, and better optimization capabilities in solving high-dimensional complex functions.

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Acknowledgements

The authors would like to thank all the reviewers for their constructive comments. This work was supported by the National Natural Science Foundation of China (61771087), the Research and Innovation Funding Project for Postgraduates of Civil Aviation University of China (2020YJS026) and the Research Foundation for Civil Aviation University of China (2020KYQD123). The program for the initialization, study, training and simulation of the proposed algorithm in this article was written with the tool-box of MATLAB 2018b produced by the Math-Works, Inc.

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Authors and Affiliations

  1. College of Electronic Information and Automation, Civil Aviation University of China, Tianjin, 300300, China

    Wu Deng, Shifan Shang, Xing Cai, Huimin Zhao & Junjie Xu

  2. Co-innovation Center of Shandong Colleges and Universities: Future Intelligent Computing, Shandong Technology and Business University, Yantai, 264005, China

    Yingjie Song

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  1. Wu Deng
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  2. Shifan Shang
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  3. Xing Cai
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  4. Huimin Zhao
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Correspondence to Huimin Zhao.

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Deng, W., Shang, S., Cai, X. et al. An improved differential evolution algorithm and its application in optimization problem. Soft Comput 25, 5277–5298 (2021). https://doi.org/10.1007/s00500-020-05527-x

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