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A Two-Operator Hybrid DE for Global Numerical Optimization

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Bio-Inspired Computing: Theories and Applications (BIC-TA 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2061))

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Abstract

Solving single objective real-parameter problem is still a challenging task. In this paper, an effective and efficient self-adaptation framework is proposed, called ToHDE, which is hybrid with CMA-ES to improve the performance. The algorithm uses two mutation strategies with linear weighted parameter to balance the exploration and exploitation. Moreover, a two-stage population size reduction and a local research are used to increase the capability of ToHDE. We evaluated the performance of ToHDE on the IEEE CEC2014 benchmark suite and compared it with six state-of-the-art peer DE variants. The statistical results show that ToHDE is competitive with the compared methods.

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Authors and Affiliations

  1. College of Information Engineering, Guizhou University of Traditional Chinese Medicine, Guiyang, 550025, China

    Xiangping Li & Yingqi Huang

Authors
  1. Xiangping Li
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  2. Yingqi Huang
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Corresponding author

Correspondence to Xiangping Li .

Editor information

Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  2. Central South University, Changsha, China

    Yong Wang

  3. Huazhong University of Science and Technology, Wuhan, China

    Jianqing Lin

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Li, X., Huang, Y. (2024). A Two-Operator Hybrid DE for Global Numerical Optimization. In: Pan, L., Wang, Y., Lin, J. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2023. Communications in Computer and Information Science, vol 2061. Springer, Singapore. https://doi.org/10.1007/978-981-97-2272-3_10

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  • DOI: https://doi.org/10.1007/978-981-97-2272-3_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2271-6

  • Online ISBN: 978-981-97-2272-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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