Abstract
Given a connected, undirected graph whose edges are labelled (or coloured), the minimum labelling spanning tree (MLST) problem seeks a spanning tree whose edges have the smallest number of distinct labels (or colours). In recent work, the MLST problem has been shown to be NP-hard and some effective heuristics have been proposed and analysed. In this paper we present preliminary results of a currently on-going project regarding the implementation of an intelligent optimization algorithm to solve the MLST problem. This algorithm is obtained by the basic Variable Neighbourhood Search heuristic with the integration of other complements from machine learning, statistics and experimental algorithmics, in order to produce high-quality performance and to completely automate the resulting optimization strategy.
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References
Chang, R.S., Leu, S.J.: The minimum labelling spanning trees. Inf. Process. Lett. 63(5), 277–282 (1997)
Consoli, S., Darby-Dowman, K., Mladenović, N., Moreno-Pérez, J.A.: Greedy randomized adaptive search and variable neighbourhood search for the minimum labelling spanning tree problem. Eur. J. Oper. Res. 196(2), 440–449 (2009)
Xiong, Y., Golden, B., Wasil, E.: A one-parameter genetic algorithm for the minimum labelling spanning tree problem. IEEE Trans. Evol. Comput. 9(1), 55–60 (2005)
Krumke, S.O., Wirth, H.C.: On the minimum label spanning tree problem. Inf. Process. Lett. 66(2), 81–85 (1998)
Xiong, Y., Golden, B., Wasil, E.: Improved heuristics for the minimum labelling spanning tree problem. IEEE Trans. Evol. Comput. 10(6), 700–703 (2006)
Cerulli, R., Fink, A., Gentili, M., Voß, S.: Metaheuristics comparison for the minimum labelling spanning tree problem. In: Golden, B.L., Raghavan, S., Wasil, E.A. (eds.) The Next Wave on Computing, Optimization, and Decision Technologies, pp. 93–106. Springer-Verlag, New York (2005)
Brüggemann, T., Monnot, J., Woeginger, G.J.: Local search for the minimum label spanning tree problem with bounded colour classes. Oper. Res. Lett. 31, 195–201 (2003)
Chwatal, A.M., Raidl, G.R.: Solving the minimum label spanning tree problem by ant colony optimization. In: Proceedings of the 7th International Conference on Genetic and Evolutionary Methods (GEM 2010), Las Vegas, Nevada (2010)
Consoli, S., Moreno-Pérez, J.A.: Solving the minimum labelling spanning tree problem using hybrid local search. In: Proceedings of the mini EURO Conference XXVIII on Variable Neighbourhood Search (EUROmC-XXVIII-VNS), Electronic Notes in Discrete Mathematics, vol. 39, pp. 75–82 (2012)
Hansen, P., Mladenović, N.: Variable neighbourhood search: principles and applications. Eur. J. Oper. Res. 130, 449–467 (2001)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)
Osman, I.H.: Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problem. Ann. Oper. Res. 41, 421–451 (1993)
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Consoli, S., Moreno Pérez, J.A., Mladenović, N. (2013). Intelligent Optimization for the Minimum Labelling Spanning Tree Problem. In: Nicosia, G., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 2013. Lecture Notes in Computer Science(), vol 7997. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44973-4_2
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DOI: https://doi.org/10.1007/978-3-642-44973-4_2
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