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  • Local search for the maximumk-plex problem

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    Pullan447217-Accepted.pdf (314.2Kb)
    Author(s)
    Pullan, Wayne
    Griffith University Author(s)
    Pullan, Wayne J.
    Year published
    2020
    Metadata
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    Abstract
    The maximum k-plex problem is an important, computationally complex graph based problem. In this study an effective k-plex local search (KLS) is presented for solving this problem on a wide range of graph types. KLS uses data structures suitable for the graph being analysed and has mechanisms for preventing search cycling and promoting search diversity. State of the art results were obtained on 121 dense graphs and 61 large real-life (sparse) graphs. Comparisons with three recent algorithms on the more difficult graphs show that KLS performed better or as well as in 93% of 332 significant k-plex problem instances investigated ...
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    The maximum k-plex problem is an important, computationally complex graph based problem. In this study an effective k-plex local search (KLS) is presented for solving this problem on a wide range of graph types. KLS uses data structures suitable for the graph being analysed and has mechanisms for preventing search cycling and promoting search diversity. State of the art results were obtained on 121 dense graphs and 61 large real-life (sparse) graphs. Comparisons with three recent algorithms on the more difficult graphs show that KLS performed better or as well as in 93% of 332 significant k-plex problem instances investigated achieving either larger average k-plex sizes (including some new results) or, when these were equivalent, lower CPU requirements.
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    Journal Title
    Journal of Heuristics
    DOI
    https://doi.org/10.1007/s10732-020-09459-5
    Copyright Statement
    © 2020 Springer Netherlands. This is an electronic version of an article published in Journal of Heuristics, 2020. Journal of Heuristics is available online at: http://link.springer.com/ with the open URL of your article.
    Note
    This publication has been entered as an advanced online version in Griffith Research Online.
    Subject
    Applied Mathematics
    Artificial Intelligence and Image Processing
    Computation Theory and Mathematics
    Science & Technology
    Computer Science, Theory & Methods
    Publication URI
    http://hdl.handle.net/10072/402306
    Collection
    • Journal articles

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