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Accelerating Artificial Bee Colony Algorithm for Global Optimization

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Neural Information Processing (ICONIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9489))

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Abstract

As an efficient optimization technique, artificial bee colony (ABC) algorithm has attracted a lot of attention for its good performance. However, ABC is good at exploration but poor at exploitation for its solution search equation. Thus, how to enhance the exploitation becomes an active research trend. In this paper, we propose a trigonometric search equation in which a hypergeometric triangle is formed to generate offspring. Additionally, the orthogonal learning strategy is integrated into the scout bee phase for generating new food source. Experiments are conducted on 23 well-known benchmark functions, and the results show that our approach has promising performance.

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Acknowledgments

This work was supported by the Foundation of State Key Laboratory of Software Engineering (No. SKLSE2014-10-04), the National Natural Science Foundation of China (Nos. 61272212 and 61462045), the Science and Technology Foundation of Jiangxi Province (Nos. 20132BAB201030 and 20151BAB217007), and the Application Research Project of Nantong Science and Technology Bureau (No. BK2014057).

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Authors and Affiliations

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

    Xinyu Zhou, Mingwen Wang & Jianyi Wan

Authors
  1. Xinyu Zhou
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  2. Mingwen Wang
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  3. Jianyi Wan
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Corresponding author

Correspondence to Xinyu Zhou .

Editor information

Editors and Affiliations

  1. University of Istanbul, Istanbul, Turkey

    Sabri Arik

  2. University at Qatar, Doha, Qatar

    Tingwen Huang

  3. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Weng Kin Lai

  4. University of Science Technology, Wuhan, China

    Qingshan Liu

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Zhou, X., Wang, M., Wan, J. (2015). Accelerating Artificial Bee Colony Algorithm for Global Optimization. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9489. Springer, Cham. https://doi.org/10.1007/978-3-319-26532-2_49

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  • DOI: https://doi.org/10.1007/978-3-319-26532-2_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26531-5

  • Online ISBN: 978-3-319-26532-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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