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Improved Differential Evolutionary Algorithm Based on Adaptive Scaling Factor

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Advances and Trends in Artificial Intelligence. Theory and Applications (IEA/AIE 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13926))

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Abstract

The differential evolution algorithm is a meta-heuristic algorithm with the advantages of a simple structure, few control parameters, and robustness. However, in the face of different problems, if the factor F controlling the difference amplification is fixed, it will be difficult for the algorithm to adapt to complex and changeable problems. In order to enable the differential evolution algorithm to deal with many problems with the optimal F parameter, this paper proposes a differential evolution algorithm with Adaptive Scaling Factor. The algorithm continuously explores the optimal F parameters for the current problem while ensuring that it does not converge prematurely, which eventually leads to an improvement in the search efficiency of the algorithm.

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Acknowledgment

This research was partially supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI under Grant JP22H03643, Japan Science and Technology Agency (JST) Support for Pioneering Research Initiated by the Next Generation (SPRING) under Grant JPMJSP2145, JST through the Establishment of University Fellowships towards the Creation of Science Technology Innovation under Grant JPMJFS2115, the National Natural Science Foundation of China grants 61802274, and Jiangsu Province Engineering Research Center of Basic Education Big Data Application.

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Authors and Affiliations

  1. Faculty of Engineering, University of Toyama, Toyama, 930-8555, Japan

    Chen Zhang, Haotian Li, Yifei Yang, Baohang Zhang & Shangce Gao

  2. School of Physics and Information Engineering, Jiangsu Second Normal University, Nanjing, China

    Huisheng Zhu

Authors
  1. Chen Zhang
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  2. Haotian Li
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  3. Yifei Yang
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  4. Baohang Zhang
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  5. Huisheng Zhu
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  6. Shangce Gao
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Corresponding author

Correspondence to Shangce Gao .

Editor information

Editors and Affiliations

  1. Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia

    Hamido Fujita

  2. Shanghai University of Finance and Economics, Shanghai, China

    Yinglin Wang

  3. Fudan University, Shanghai, China

    Yanghua Xiao

  4. Texas State University, San Marcos, TX, USA

    Ali Moonis

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Zhang, C., Li, H., Yang, Y., Zhang, B., Zhu, H., Gao, S. (2023). Improved Differential Evolutionary Algorithm Based on Adaptive Scaling Factor. In: Fujita, H., Wang, Y., Xiao, Y., Moonis, A. (eds) Advances and Trends in Artificial Intelligence. Theory and Applications. IEA/AIE 2023. Lecture Notes in Computer Science(), vol 13926. Springer, Cham. https://doi.org/10.1007/978-3-031-36822-6_15

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  • DOI: https://doi.org/10.1007/978-3-031-36822-6_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-36821-9

  • Online ISBN: 978-3-031-36822-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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