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A Divisive Multi-level Differential Evolution

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Computational Intelligence and Intelligent Systems (ISICA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 874))

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Abstract

It is generally accepted that the clustering-based differential evolution (CDE) algorithm exhibits better performance in comparison with the standard differential evolution. However, such clustering method mechanism that is only based on input data may lead to some limitations such as premature convergence. In this study, we propose a divisive multi-level differential evolution algorithm (DMDE) to alleviate this drawback. The proposed divisive method is based not only input data but also the output fitness. In particular, DMDE becomes the conventional CDE when the output fitness in not considered in the process of clustering. Several benchmark functions are included to evaluate the performance of the proposed DMDE. Experimental results show that the proposed DMDE exhibits a promising performance when compared with CDE, especially in case of high-dimensional continuous optimization problems.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant no. 61673295), and supported by the Tianjin Science and Technology Major Project (Grant no.15ZXZNCX00050).

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Authors and Affiliations

  1. Tianjin Key Laboratory of Intelligent and Novel Software Technology, Tianjin University of Technology, Tianjin, 300384, China

    Huifang Zhang, Wei Huang & Jinsong Wang

  2. School of Computer and Communication Engineering, Tianjin University of Technology, Tianjin, 300384, China

    Huifang Zhang, Wei Huang & Jinsong Wang

Authors
  1. Huifang Zhang
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  2. Wei Huang
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  3. Jinsong Wang
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Corresponding author

Correspondence to Wei Huang .

Editor information

Editors and Affiliations

  1. College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China

    Kangshun Li

  2. Jiangxi University of Science and Technology, Ganzhou, Jiangxi, China

    Wei Li

  3. Chemical and Petroleum Engineering, University of Calgary, Calgary, Alberta, Canada

    Zhangxing Chen

  4. School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yong Liu

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Zhang, H., Huang, W., Wang, J. (2018). A Divisive Multi-level Differential Evolution. In: Li, K., Li, W., Chen, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2017. Communications in Computer and Information Science, vol 874. Springer, Singapore. https://doi.org/10.1007/978-981-13-1651-7_8

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  • DOI: https://doi.org/10.1007/978-981-13-1651-7_8

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1650-0

  • Online ISBN: 978-981-13-1651-7

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