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    An Artificial Bee Colony algorithm with guide of global & local optima and asynchronous scaling factors for numerical optimization

    Access Status
    Fulltext not available
    Authors
    Liu, J.
    Zhu, H.
    Ma, Q.
    Zhang, L.
    Xu, Honglei
    Date
    2015
    Type
    Journal Article
    
    Metadata
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    Citation
    Liu, J. and Zhu, H. and Ma, Q. and Zhang, L. and Xu, H. 2015. An Artificial Bee Colony algorithm with guide of global & local optima and asynchronous scaling factors for numerical optimization. Applied Soft Computing. 37: pp. 608-618.
    Source Title
    Applied Soft Computing
    DOI
    10.1016/j.asoc.2015.08.021
    ISSN
    1568-4946
    School
    Department of Mathematics and Statistics
    URI
    http://hdl.handle.net/20.500.11937/23444
    Collection
    • Curtin Research Publications
    Abstract

    Artificial Bee Colony (ABC) algorithm is a wildly used optimization algorithm. However, ABC is excellent in exploration but poor in exploitation. To improve the convergence performance of ABC and establish a better searching mechanism for the global optimum, an improved ABC algorithm is proposed in this paper. Firstly, the proposed algorithm integrates the information of previous best solution into the search equation for employed bees and global best solution into the update equation for onlooker bees to improve the exploitation. Secondly, for a better balance between the exploration and exploitation of search, an S-type adaptive scaling factors are introduced in employed bees’ search equation. Furthermore, the searching policy of scout bees is modified. The scout bees need update food source in each cycle in order to increase diversity and stochasticity of the bees and mitigate stagnation problem. Finally, the improved algorithms is compared with other two improved ABCs and three recent algorithms on a set of classical benchmark functions. The experimental results show that our proposed algorithm is effective and robust and outperform the other algorithms.

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