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  • Chaotic gravitational constants for the gravitational search algorithm

    Author(s)
    Mirjalili, Seyedali
    Gandomi, Amir H
    Griffith University Author(s)
    Mirjalili, Seyedali
    Year published
    2017
    Metadata
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    Abstract
    In a population-based meta-heuristic, the search process is divided into two main phases: exploration versus exploitation. In the exploration phase, a random behavior is fruitful to explore the search space as extensive as possible. In contrast, a fast exploitation toward the promising regions is the main objective of the latter phase. It is really challenging to find a proper balance between these two phases because of the stochastic nature of population-based meta-heuristic algorithms. The literature shows that chaotic maps are able to improve both phases. This work embeds ten chaotic maps into the gravitational constant ...
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    In a population-based meta-heuristic, the search process is divided into two main phases: exploration versus exploitation. In the exploration phase, a random behavior is fruitful to explore the search space as extensive as possible. In contrast, a fast exploitation toward the promising regions is the main objective of the latter phase. It is really challenging to find a proper balance between these two phases because of the stochastic nature of population-based meta-heuristic algorithms. The literature shows that chaotic maps are able to improve both phases. This work embeds ten chaotic maps into the gravitational constant (G) of the recently proposed population-based meta-heuristic algorithm called Gravitational Search Algorithm (GSA). Also, an adaptive normalization method is proposed to transit from the exploration phase to the exploitation phase smoothly. As case studies, twelve shifted and biased benchmark functions evaluate the performance of the proposed chaos-based GSA algorithms in terms of exploration and exploitation. A statistical test called Wilcoxon rank-sum is done to judge about the significance of the results as well. The results demonstrate that sinusoidal map is the best map for improving the performance of GSA significantly.
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    Journal Title
    Applied Soft Computing
    Volume
    53
    DOI
    https://doi.org/10.1016/j.asoc.2017.01.008
    Subject
    Artificial intelligence
    Applied mathematics
    Applied mathematics not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/341330
    Collection
    • Journal articles

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