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Self-adapting Differential Evolution Algorithm with Chaos Random for Global Numerical Optimization

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Advances in Computation and Intelligence (ISICA 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6382))

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Abstract

Choosing the proper control parameters for DE is quite difficult because the best settings for the control parameters can be different for different functions. In this paper, the proposed self-adaptive method is an attempt to determine the values of control parameters F and CR. In this method, the adjusting of F and CR associates with fitness of individuals and the new values are Chaos random numbers. The experiment results show that this algorithm can attain better solutions than other algorithms for multimodal functions.

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Author information

Authors and Affiliations

  1. School of Computer Science, China University of Geosciences, Wuhan, 430074, China

    Ming Yang & Zhihua Cai

  2. Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, 430074, China

    Jing Guan

  3. Department of Computer Engineering, Ordnance Engineering College, Shijiazhuang, 050003, China

    Lu Wang

Authors
  1. Ming Yang
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  2. Jing Guan
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  3. Zhihua Cai
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  4. Lu Wang
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Editor information

Editors and Affiliations

  1. School of Computer Science, China University of Geosciences, 430074, Wuhan, Hubei, P.R. China

    Zhihua Cai

  2. School of Computer Science, China University of Geosciences, Wu-Han, China University of Geosciences, 430074, Wuhan, Hubei, P.R. China

    Chengyu Hu

  3. Computation Center, Wuhan University, 430072, Wuhan, Hubei, China

    Zhuo Kang

  4. School of Computer Science and Engineering, The University of Aizu, Tsuruga, Ikki-machi, 965-8580, Aizu-Wakamatsu City, Fukushima, Japan

    Yong Liu

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Yang, M., Guan, J., Cai, Z., Wang, L. (2010). Self-adapting Differential Evolution Algorithm with Chaos Random for Global Numerical Optimization. In: Cai, Z., Hu, C., Kang, Z., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2010. Lecture Notes in Computer Science, vol 6382. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16493-4_12

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  • DOI: https://doi.org/10.1007/978-3-642-16493-4_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16492-7

  • Online ISBN: 978-3-642-16493-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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