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Gaussian Cauchy Differential Evolution for Global Optimization

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Artificial Intelligence (ICAI 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 888))

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Abstract

Differential evolution (DE) has been proven to be a powerful and efficient stochastic search technique for global numerical optimization. However, choosing the optimal control parameters of DE is a time-consuming task because they are problem depended. DE may have a strong ability in exploring the search space and locating the promising area of global optimum but may be slow at exploitation. Thus, in this paper, we propose a Gaussian Cauchy differential evolution (GCDE). It is a hybrid of a modified bare-bones swarm optimizers and the differential evolution algorithm. It takes advantage of the good exploration searching ability of DE and the good exploitation ability of bare-bones optimization. Moreover, the parameters in GCDE are generated by the function of Gaussian distribution and Cauchy distribution. In addition, the parameters dynamically change according to the quality of the current search solution. The performance of proposed method is compared with three differential evolution algorithms and three bare-bones technique based optimizers. Comprehensive experimental results show that the proposed approach is better than, or at least comparable to, other classic DE variants when considering the quality of search solutions on a set of benchmark problems.

Supported by the National Natural Science Foundation of China (61572298, 61772322, 61573166) and the Key Research and Development Foundation of Shandong Province (2017GGX1011).

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Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Jinan, China

    Qingke Zhang & Huaxiang Zhang

  2. Postdoctoral Research Station of Management Science and Engineering, Shandong Normal University, Jinan, China

    Qingke Zhang & Huaxiang Zhang

  3. Shandong Provincial Key Laboratory of Network based Intelligent Computing, University of Jinan, Jinan, China

    Bo Yang

  4. School of Computer Science and Technology, Shandong University, Jinan, China

    Yupeng Hu

Authors
  1. Qingke Zhang
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  2. Huaxiang Zhang
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  3. Bo Yang
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  4. Yupeng Hu
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Corresponding authors

Correspondence to Qingke Zhang or Huaxiang Zhang .

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Editors and Affiliations

  1. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  2. Hong Kong University of Science and Technology, Hong Kong SAR, China

    Qiang Yang

  3. Nanjing University, Nanjing, China

    Yang Gao

  4. JD Finance, Beijing, China

    Yu Zheng

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Zhang, Q., Zhang, H., Yang, B., Hu, Y. (2018). Gaussian Cauchy Differential Evolution for Global Optimization. In: Zhou, ZH., Yang, Q., Gao, Y., Zheng, Y. (eds) Artificial Intelligence. ICAI 2018. Communications in Computer and Information Science, vol 888. Springer, Singapore. https://doi.org/10.1007/978-981-13-2122-1_13

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  • DOI: https://doi.org/10.1007/978-981-13-2122-1_13

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2121-4

  • Online ISBN: 978-981-13-2122-1

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