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Title: Greedy Randomized Adaptive Search and Variable Neighbourhood Search for the minimum labelling spanning tree problem
Authors: Consoli, S
Darby-Dowman, K
Mladenović, N
Moreno-Pérez, JA
Keywords: Metaheuristics;Combinatorial optimisation;Minimum labelling spanning tree;Variable Neighbourhood Search (VNS);Greedy Randomized Adaptive Search Procedure (GRASP)
Issue Date: 2009
Publisher: Elsevier
Citation: European Journal of Operational Research. 196(2): 440-449
Abstract: This 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.
URI: journaldescription.cws_home/505543/description#description
Appears in Collections:Publications
Dept of Mathematics Research Papers
Mathematical Sciences

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