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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/9822

    Título
    Dataset of the paper “A stepped tabu search method for the clique partitioning problem”. Applied Intelligence, 53, 16275-16292
    Autor
    Pacheco Bonrostro, JoaquínUBU authority Orcid
    Casado Yusta, SilviaUBU authority Orcid
    Editorial
    Universidad de Burgos
    Fecha de publicación
    2022
    DOI
    10.71486/5n2k-c575
    Abstract
    Given an undirected graph, a clique is a subset of vertices in which the induced subgraph is complete; that is, all pairs of vertices of this subset are adjacent. Clique problems in graphs are very important due to their numerous applications. One of these problems is the clique partitioning problem (CPP), which consists of dividing the set of vertices of a graph into the smallest number of cliques possible. The CPP is an NP-hard problem with many application fields (timetabling, manufacturing, scheduling, telecommunications, etc.). Despite its great applicability, few recent studies have focused on proposing specific resolution methods for the CPP. This article presents a resolution method that combines multistart strategies with tabu search. The most novel characteristic of our method is that it allows unfeasible solutions to be visited, which facilitates exploration of the solution space. The computational tests show that our method performs better than previous methods proposed for this problem. In fact, our method strictly improves the results of these methods in most of the instances considered while requiring less computation time.
    Palabras clave
    Clique partitioning problem
    Metaheuristics
    Tabu search
    Multistart methods
    Materia
    Investigación operativa
    Operations research
    Modelos matemáticos
    Mathematical models
    URI
    http://hdl.handle.net/10259/9822
    Referenciado en
    http://hdl.handle.net/10259/7402
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