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Indicators Directed Multi-strategy Artificial Bee Colony Algorithm

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International Conference on Neural Computing for Advanced Applications (NCAA 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1869))

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Abstract

Artificial bee colony (ABC) algorithm is commonly used to solve various optimization problems. Though ABC shows strong exploration capability, its weak exploitation may easily result in slow convergence. To tackle this issue, several multi-strategy ABC variants were proposed. Employing multiple search strategies with distinct features facilitates an appropriate balance between exploration and exploitation. However, choosing an appropriate strategy for the current search is a difficult task. This article suggests a new ABC variant named indicators directed multi-strategy ABC (IDMABC) to address this issue. Three evaluation indicators are designed to help ABC adaptively select suitable search strategies during the evolution. To validate the optimization capability of IDMABC, 22 classical problems are tested. IDMABC is evaluated against five other ABC variants. Results show the competitiveness of IDMABC.

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Acknowledgments

This work was supported by Jiangxi Provincial Natural Science Foundation (Nos. 20212BAB202023 and 20212BAB202022).

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Authors and Affiliations

  1. School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China

    Jiali Wu, Jiawen Liu, Hui Wang, Hai Zhang & Min Hu

  2. School of Electrical Engineering, Nanchang Institute of Technology, Nanchang, 330099, China

    Jingwu Wang

  3. School of Business Administration, Nanchang Institute of Technology, Nanchang, 330099, China

    Wenjun Wang

Authors
  1. Jiali Wu
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  2. Jingwu Wang
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  3. Wenjun Wang
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  4. Jiawen Liu
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  5. Hui Wang
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  6. Hai Zhang
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  7. Min Hu
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Corresponding author

Correspondence to Hui Wang .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, China

    Haijun Zhang

  2. Chaohu University, Hefei, China

    Yinggen Ke

  3. Chongqing University, Chongqing, China

    Zhou Wu

  4. South China Normal University, Guangzhou, China

    Tianyong Hao

  5. Hefei University of Technology, Hefei, China

    Zhao Zhang

  6. Technical University of Denmark, Kongens Lyngby, Denmark

    Weizhi Meng

  7. Chaohu University, Hefei, China

    Yuanyuan Mu

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Wu, J. et al. (2023). Indicators Directed Multi-strategy Artificial Bee Colony Algorithm. In: Zhang, H., et al. International Conference on Neural Computing for Advanced Applications. NCAA 2023. Communications in Computer and Information Science, vol 1869. Springer, Singapore. https://doi.org/10.1007/978-981-99-5844-3_20

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  • DOI: https://doi.org/10.1007/978-981-99-5844-3_20

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-5843-6

  • Online ISBN: 978-981-99-5844-3

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