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Neighborhood Learning for Artificial Bee Colony Algorithm: A Mini-survey

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Neural Information Processing (ICONIP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14449))

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Abstract

Artificial bee colony (ABC) algorithm is a representative paradigm of swarm intelligence optimization (SIO) algorithms, which has received much attention in the field of global optimization for its good performance yet simple structure. However, there still exists a drawback for ABC that it owns strong exploration but weak exploitation, resulting in slow convergence speed and low convergence accuracy. To solve this drawback, in recent years, the neighborhood learning mechanism has emerged as an effective method, becoming a hot research topic in the community of ABC. However, there has been no surveys on it, even a short one. Considering the appeal of the neighborhood learning mechanism, we are motivated to provide a mini-survey to highlight some key aspects about it, including 1) how to construct a neighborhood topology? 2) how to select the learning exemplar? and 3) what are the advantages and disadvantages? In this mini-survey, some related neighborhood-based ABC variants are reviewed to reveal the key aspects. Furthermore, some interesting future research directions are also given to encourage deeper related works.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (Nos. 61966019 and 62366022), the Jiangxi Provincial Natural Science Foundation (No. 20232BAB202048), and the Science and Technology Plan Projects of Jiangxi Provincial Education Department (No. GJJ210324).

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Authors and Affiliations

  1. School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, 330022, China

    Xinyu Zhou, Guisen Tan, Yanlin Wu & Shuixiu Wu

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  1. Xinyu Zhou
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  2. Guisen Tan
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  3. Yanlin Wu
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  4. Shuixiu Wu
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Correspondence to Xinyu Zhou .

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Editors and Affiliations

  1. Central South University, Changsha, China

    Biao Luo

  2. Chinese Academy of Sciences, Beijing, China

    Long Cheng

  3. Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

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Zhou, X., Tan, G., Wu, Y., Wu, S. (2024). Neighborhood Learning for Artificial Bee Colony Algorithm: A Mini-survey. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14449. Springer, Singapore. https://doi.org/10.1007/978-981-99-8067-3_28

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  • DOI: https://doi.org/10.1007/978-981-99-8067-3_28

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