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dc.contributor.authorConsoli, S-
dc.contributor.authorDarby-Dowman, K-
dc.contributor.authorMladenović, N-
dc.contributor.authorMoreno-Pérez, JA-
dc.identifier.citationEuropean Journal of Operational Research. 196(2): 440-449en
dc.identifier.uri journaldescription.cws_home/505543/description#descriptionen
dc.description.abstractThis paper studies heuristics for the minimum labelling spanning tree (MLST) problem. The purpose is to find a spanning tree using edges that are as similar as possible. Given an undirected labelled connected graph, the minimum labelling spanning tree problem seeks a spanning tree whose edges have the smallest number of distinct labels. This problem has been shown to be NP-hard. A Greedy Randomized Adaptive Search Procedure (GRASP) and a Variable Neighbourhood Search (VNS) are proposed in this paper. They are compared with other algorithms recommended in the literature: the Modified Genetic Algorithm and the Pilot Method. Nonparametric statistical tests show that the heuristics based on GRASP and VNS outperform the other algorithms tested. Furthermore, a comparison with the results provided by an exact approach shows that we may quickly obtain optimal or near-optimal solutions with the proposed heuristics.en
dc.format.extent279148 bytes-
dc.subjectCombinatorial optimisation-
dc.subjectMinimum labelling spanning tree-
dc.subjectVariable Neighbourhood Search (VNS)-
dc.subjectGreedy Randomized Adaptive Search Procedure (GRASP)-
dc.titleGreedy Randomized Adaptive Search and Variable Neighbourhood Search for the minimum labelling spanning tree problemen
dc.typeResearch Paperen
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Dept of Mathematics Research Papers
Mathematical Sciences

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