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Self-adaptive differential evolution with global neighborhood search

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Abstract

Differential evolution (DE) is a simple yet efficient stochastic search approach for numerical optimization. However, it tends to suffer from slow convergence when tackling complicated problems. In addition, its search ability is significantly influenced by its control parameters. To improve the performance of the basic DE, this paper proposes a self-adaptive differential evolution with global neighborhood search (NSSDE). In the proposed NSSDE, its control parameters are self-adaptively tuned according to the feedback from the search process, while the global neighborhood search strategy is incorporated to accelerate the convergence speed. To evaluate the performance of the proposed NSSDE, we compare it with several DE variants on a set of benchmark test functions. The experimental results show that NSSDE can achieve better results than its competitors on the majority of the benchmark test functions.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (Nos. 61563019, 61300127, and 61402481), by Natural Science Foundation of Jiangxi, China (Nos. 20151BAB217010 and 20151BAB201015).

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Authors and Affiliations

  1. Institute of Medical Informatics and Engineering, School of Science, JiangXi University of Science and Technology, Ganzhou, 341000, China

    Zhaolu Guo

  2. Computer School, Hubei University of Technology, Wuhan, 430072, China

    Gang Liu

  3. School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China

    Dahai Li

  4. School of Information Engineering, Shijiazhuang University of Economics, Shijiazhuang, 050031, China

    Shenwen Wang

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  1. Zhaolu Guo
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  2. Gang Liu
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  3. Dahai Li
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  4. Shenwen Wang
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Correspondence to Zhaolu Guo.

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The authors declare that there is no conflict of interests regarding the publication of this paper.

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Communicated by V. Loia.

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Cite this article

Guo, Z., Liu, G., Li, D. et al. Self-adaptive differential evolution with global neighborhood search. Soft Comput 21, 3759–3768 (2017). https://doi.org/10.1007/s00500-016-2029-x

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  • DOI: https://doi.org/10.1007/s00500-016-2029-x

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