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  • Fractional-order calculus-based flower pollination algorithm with local search for global optimization and image segmentation

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    Mirjalili421870-Accepted.pdf (14.40Mb)
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    Accepted Manuscript (AM)
    Author(s)
    Yousri, Dalia
    Abd Elaziz, Mohamed
    Mirjalili, Seyedali
    Griffith University Author(s)
    Mirjalili, Seyedali
    Year published
    2020
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    Abstract
    Introducing a novel approach to enhance the diversification and intensification propensities of the flower pollination algorithm (FPA) is the main aim of this paper. Therefore, the fractional-order (FO) calculus features are adopted to enhance the basic FPA local search ability and adaptive modify the harmonization coefficient among the FPA exploration and exploitation cores. The proposed Fractional-order FPA (FO-FPA) is examined in a number of experiments. Firstly, FO-FPA is tested with thirty-six benchmark functions with several dimensions. The proposed FO-FPA is compared with recent proved techniques based on several ...
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    Introducing a novel approach to enhance the diversification and intensification propensities of the flower pollination algorithm (FPA) is the main aim of this paper. Therefore, the fractional-order (FO) calculus features are adopted to enhance the basic FPA local search ability and adaptive modify the harmonization coefficient among the FPA exploration and exploitation cores. The proposed Fractional-order FPA (FO-FPA) is examined in a number of experiments. Firstly, FO-FPA is tested with thirty-six benchmark functions with several dimensions. The proposed FO-FPA is compared with recent proved techniques based on several statistical measures and non parametric tests. Secondly, FO-FPA is implemented for a real application of the image segmentation and its results compared with state-of-the-art multi-level thresholding algorithms. The comparisons divulge the remarkable influence of merging FO with the basic FPA in improving the quality of the solutions and the acceleration of the convergence speed.
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    Journal Title
    Knowledge-Based Systems
    Volume
    197
    DOI
    https://doi.org/10.1016/j.knosys.2020.105889
    Copyright Statement
    © 2020 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Psychology
    Science & Technology
    Computer Science, Artificial Intelligence
    Flower pollination algorithm
    Publication URI
    http://hdl.handle.net/10072/394933
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    • Journal articles

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