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DC Field | Value | Language |
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dc.contributor.author | Consoli, S | - |
dc.contributor.author | Darby-Dowman, K | - |
dc.contributor.author | Mladenović, N | - |
dc.contributor.author | Moreno-Pérez, JA | - |
dc.date.accessioned | 2014-12-23T13:57:30Z | - |
dc.date.available | 2014-12-23T13:57:30Z | - |
dc.date.issued | 2007 | - |
dc.identifier.citation | Department of Mathematical Sciences Technical Reports, TR/01/07, May 2007 | en_US |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/9629 | - |
dc.description.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-complete. A Greedy Randomized Adaptive Search Procedure (GRASP) and different versions of Variable Neighbourhood Search (VNS) are proposed. 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_US |
dc.language.iso | en | en_US |
dc.publisher | Brunel University | en_US |
dc.subject | Metaheuristics | en_US |
dc.subject | Combinatorial optimization | en_US |
dc.subject | Minimum labelling spanning tree | en_US |
dc.subject | Variable neighbourhood search | en_US |
dc.subject | Greedy randomized adaptive search procedure | en_US |
dc.title | Heuristics based on greedy randomized adaptive search and variable neighbourhood search for the minimum labelling spanning tree problem | en_US |
dc.type | Technical Report | en_US |
Appears in Collections: | Dept of Mathematics Research Papers |
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Fulltext.pdf | 708.06 kB | Adobe PDF | View/Open |
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