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  • Two Weighting Local Search for Minimum Vertex Cover

    Author(s)
    Su, Kaile
    Cai, Shaowei
    Lin, Jinkun
    Griffith University Author(s)
    Su, Kaile
    Year published
    2015
    Metadata
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    Abstract
    Minimum Vertex Cover (MinVC) is a well known NP-hard combinatorial optimization problem, and local search has been shown to be one of the most effective approaches to this problem. State-of-the-art MinVC local search algorithms employ edge weighting techniques and prefer to select vertices with higher weighted score. These algorithms are not robust and especially have poor performance on instances with structures which defeat greedy heuristics. In this paper, we propose a vertex weighting scheme to address this shortcoming, and combine it within the current best MinVC local search algorithm NuMVC, leading to a new algorithm ...
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    Minimum Vertex Cover (MinVC) is a well known NP-hard combinatorial optimization problem, and local search has been shown to be one of the most effective approaches to this problem. State-of-the-art MinVC local search algorithms employ edge weighting techniques and prefer to select vertices with higher weighted score. These algorithms are not robust and especially have poor performance on instances with structures which defeat greedy heuristics. In this paper, we propose a vertex weighting scheme to address this shortcoming, and combine it within the current best MinVC local search algorithm NuMVC, leading to a new algorithm called TwMVC. Our experiments show that TwMVC outperforms NuMVC on the standard benchmarks namely DIMACS and BHOSLIB. To the best of our knowledge, TwMVC is the first MinVC algorithm that attains the best known solution for all instances in both benchmarks. Further, TwMVC shows superiority on a benchmark of real-world networks.
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    Conference Title
    Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence
    Publisher URI
    https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9311
    Subject
    Artificial Intelligence and Image Processing not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/140002
    Collection
    • Conference outputs

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