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Improving Artificial Bee Colony Algorithm with Historical Archive

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Bio-inspired Computing – Theories and Applications (BIC-TA 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 682))

Abstract

In this study, an artificial bee colony with historical archive (HAABC) is proposed to help ABC escape from stagnation situation. The proposed framework keeps track of the search history and stores excellent successful solutions into an archive. Once stagnation is detected in scout bees phase of ABC, a new individual is generated by utilizing the historical archive. Experimental results on 28 benchmark functions show that the proposed framework significantly improves the performance of basic ABC and five state-of-the-art ABC algorithms.

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Acknowledgments

This work is supported by Foundation for Distinguished Young Talents in Higher Education of Guangdong, China (Yqgdufe1404), and Program for Characteristic Innovation Talents of Guangdong (2014KTSCX127), and the National Natural Science Foundation of China (61472453, 61673403).

Author information

Authors and Affiliations

  1. College of Information, Guangdong University of Finance & Economics, Guangzhou, 510320, China

    Yalan Zhou

  2. Department of Computer Science, Sun Yat-sen University, Guangzhou, 510006, China

    Jiahai Wang, Xing Yang & Jian Yin

  3. Guangdong Province Key Laboratory of Big Data Analysis and Processing, Guangzhou, 510006, China

    Jiahai Wang & Jian Yin

  4. Faculty of Engineering, University of Toyama, Toyama-shi, 930-8555, Japan

    Shangce Gao

Authors
  1. Yalan Zhou
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  2. Jiahai Wang
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  3. Shangce Gao
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  4. Xing Yang
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  5. Jian Yin
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Corresponding author

Correspondence to Jiahai Wang .

Editor information

Editors and Affiliations

  1. Xidian University , Xi’an, China

    Maoguo Gong

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

    Linqiang Pan

  3. China University of Petroleum, Qingdao, China

    Tao Song

  4. Southwest Jiaotong University , Chengdu, China

    Gexiang Zhang

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© 2016 Springer Nature Singapore Pte Ltd.

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Zhou, Y., Wang, J., Gao, S., Yang, X., Yin, J. (2016). Improving Artificial Bee Colony Algorithm with Historical Archive. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 682. Springer, Singapore. https://doi.org/10.1007/978-981-10-3614-9_24

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  • DOI: https://doi.org/10.1007/978-981-10-3614-9_24

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3613-2

  • Online ISBN: 978-981-10-3614-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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