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  • Biology migration algorithm: a new nature-inspired heuristic methodology for global optimization

    Author(s)
    Zhang, Qingyang
    Wang, Ronggui
    Yang, Juan
    Lewis, Andrew
    Chiclana, Francisco
    Yang, Shengxiang
    Griffith University Author(s)
    Lewis, Andrew J.
    Year published
    2019
    Metadata
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    Abstract
    In this paper, inspired by the biology migration phenomenon, which is ubiquitous in the social evolution process in nature, a new meta-heuristic optimization paradigm called biology migration algorithm (BMA) is proposed. This optimizer consists of two phases, i.e., migration phase and updating phase. The first phase mainly simulates how the species move to new habits. During this phase, each agent should obey two main rules depicted by two random operators. The second phase mimics how some species leave the group and new ones join the group during the migration process. In this phase, a maximum number of iterations will be ...
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    In this paper, inspired by the biology migration phenomenon, which is ubiquitous in the social evolution process in nature, a new meta-heuristic optimization paradigm called biology migration algorithm (BMA) is proposed. This optimizer consists of two phases, i.e., migration phase and updating phase. The first phase mainly simulates how the species move to new habits. During this phase, each agent should obey two main rules depicted by two random operators. The second phase mimics how some species leave the group and new ones join the group during the migration process. In this phase, a maximum number of iterations will be set to predetermine whether a current individual should leave and be replaced by a new one. Simulation results based on a comprehensive set of benchmark functions and four real engineering problems indicate that BMA is effective in comparison with other existing optimization methods.
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    Journal Title
    Soft Computing
    Volume
    23
    Issue
    16
    DOI
    https://doi.org/10.1007/s00500-018-3381-9
    Subject
    Applied Mathematics
    Artificial Intelligence and Image Processing
    Cognitive Sciences
    Publication URI
    http://hdl.handle.net/10072/382960
    Collection
    • Journal articles

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