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An Improved Artificial Bee Colony Algorithm for Solving Extremal Optimization of Function Problem

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Intelligent Computing Theories and Application (ICIC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9771))

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Abstract

Some defects of artificial bee colony algorithm such as low efficiency, slow convergence rate, may lead to a fall into local optimum. In order to deal with these problems, some thoughts in genetic algorithm are introduced to improve bee colony algorithm in this paper. Specifically, a factor, which used to memorize the current global optimal position, is added to the follower bee operator to improve the global convergence speed and accuracy of the bee colony algorithm. Additionally, inertia factor and search factor are also adopted to change the proportion between the factors that affect the global convergence speed and local convergence speed, in order to accelerate the speed of bee colony algorithm applied in function extremum optimization. The experimental results show that the improved algorithm leads to fast convergence, high efficiency and robust performance.

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Acknowledgments

This work was supported by the Scientific Research Project of Hechi University (No. XJ2016KQ01), National Undergraduate Training Programs for Innovation and Entrepreneurship (No. 201610605029), Open Foundation of Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis (No. HCIC201411), and the Education Scientific Research Foundation of Guangxi Province (No. KY2015YB254).

Author information

Authors and Affiliations

  1. College of Computer and Information Engineering, Hechi University, Yizhou, 546300, China

    Yunfei Yi, Yangqian Su & Jian Miao

  2. Computer School, Wuhan University, Wuhan, 43007, China

    Gang Fang

  3. Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence, Nanning, 530006, China

    Yunfei Yi

  4. Hechi Municipal People’s Government Office, Hechi, 547000, China

    Zhi Yin

Authors
  1. Yunfei Yi
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  2. Gang Fang
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  3. Yangqian Su
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  4. Jian Miao
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  5. Zhi Yin
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Corresponding author

Correspondence to Jian Miao .

Editor information

Editors and Affiliations

  1. Tongji University , Shanghai, China

    De-Shuang Huang

  2. Polytechnic of Bari , Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong , North Wollongong, New South Wales, Australia

    Prashan Premaratne

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© 2016 Springer International Publishing Switzerland

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Yi, Y., Fang, G., Su, Y., Miao, J., Yin, Z. (2016). An Improved Artificial Bee Colony Algorithm for Solving Extremal Optimization of Function Problem. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9771. Springer, Cham. https://doi.org/10.1007/978-3-319-42291-6_70

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  • DOI: https://doi.org/10.1007/978-3-319-42291-6_70

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42290-9

  • Online ISBN: 978-3-319-42291-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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