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  • Clustering analysis using a novel locality-informed grey wolf-inspired clustering approach

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    MIRJALILI220474.pdf (1.966Mb)
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    Accepted Manuscript (AM)
    Author(s)
    Aljarah, Ibrahim
    Mafarja, Majdi
    Heidari, Ali Asghar
    Faris, Hossam
    Mirjalili, Seyedali
    Griffith University Author(s)
    Mirjalili, Seyedali
    Year published
    2020
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    Abstract
    Grey wolf optimizer (GWO) is known as one of the recent popular metaheuristic algorithms inspired from the social collaboration and team hunting activities of grey wolves in nature. This algorithm benefits from stochastic operators, but it is still prone to stagnation in local optima and premature convergence when solving problems with a large number of variables (e.g., clustering problems). To alleviate this shortcoming, the GWO algorithm is hybridized with the well-known tabu search (TS). To investigate the performance of the proposed hybrid GWO and TS (GWOTS), it is compared with well-regarded metaheuristics on various ...
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    Grey wolf optimizer (GWO) is known as one of the recent popular metaheuristic algorithms inspired from the social collaboration and team hunting activities of grey wolves in nature. This algorithm benefits from stochastic operators, but it is still prone to stagnation in local optima and premature convergence when solving problems with a large number of variables (e.g., clustering problems). To alleviate this shortcoming, the GWO algorithm is hybridized with the well-known tabu search (TS). To investigate the performance of the proposed hybrid GWO and TS (GWOTS), it is compared with well-regarded metaheuristics on various clustering datasets. The comprehensive experiments and analysis verify that the proposed GWOTS shows an improved performance compared to GWO and can be utilized for clustering applications.
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    Journal Title
    Knowledge and Information Systems
    DOI
    https://doi.org/10.1007/s10115-019-01358-x
    Copyright Statement
    © 2019 Springer London. This is an electronic version of an article published in Knowledge and Information Systems, AOV. Knowledge and Information Systems is available online at: http://link.springer.com// with the open URL of your article.
    Note
    This publication has been entered into Griffith Research Online as an Advanced Online Version.
    Subject
    Artificial intelligence
    Publication URI
    http://hdl.handle.net/10072/385781
    Collection
    • Journal articles

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