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A GRASP algorithm for the multi-criteria minimum spanning tree problem

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  • José Arroyo
  • Pedro Vieira
  • Dalessandro Vianna

Abstract

This paper proposes a GRASP (Greedy Randomized Adaptive Search Procedure) algorithm for the multi-criteria minimum spanning tree problem, which is NP-hard. In this problem a vector of costs is defined for each edge of the graph and the problem is to find all Pareto optimal or efficient spanning trees (solutions). The algorithm is based on the optimization of different weighted utility functions. In each iteration, a weight vector is defined and a solution is built using a greedy randomized constructive procedure. The found solution is submitted to a local search trying to improve the value of the weighted utility function. We use a drop-and-add neighborhood where the spanning trees are represented by Prufer numbers. In order to find a variety of efficient solutions, we use different weight vectors, which are distributed uniformly on the Pareto frontier. The proposed algorithm is tested on problems with r=2 and 3 criteria. For non-complete graphs with n=10, 20 and 30 nodes, the performance of the algorithm is tested against a complete enumeration. For complete graphs with n=20, 30 and 50 nodes the performance of the algorithm is tested using two types of weighted utility functions. The algorithm is also compared with the multi-criteria version of the Kruskal’s algorithm, which generates supported efficient solutions. Copyright Springer Science+Business Media, LLC 2008

Suggested Citation

  • José Arroyo & Pedro Vieira & Dalessandro Vianna, 2008. "A GRASP algorithm for the multi-criteria minimum spanning tree problem," Annals of Operations Research, Springer, vol. 159(1), pages 125-133, March.
    • Handle: RePEc:spr:annopr:v:159:y:2008:i:1:p:125-133:10.1007/s10479-007-0263-4
    DOI: 10.1007/s10479-007-0263-4
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    References listed on IDEAS

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    1. J E C Arroyo & V A Armentano, 2004. "A partial enumeration heuristic for multi-objective flowshop scheduling problems," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(9), pages 1000-1007, September.
    2. Ramos, R. M. & Alonso, S. & Sicilia, J. & Gonzalez, C., 1998. "The problem of the optimal biobjective spanning tree," European Journal of Operational Research, Elsevier, vol. 111(3), pages 617-628, December.
    3. Ehrgott, Matthias & Klamroth, Kathrin, 1997. "Connectedness of efficient solutions in multiple criteria combinatorial optimization," European Journal of Operational Research, Elsevier, vol. 97(1), pages 159-166, February.
    4. Zhou, Gengui & Gen, Mitsuo, 1999. "Genetic algorithm approach on multi-criteria minimum spanning tree problem," European Journal of Operational Research, Elsevier, vol. 114(1), pages 141-152, April.
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    Cited by:

    1. Carolina Almeida & Richard Gonçalves & Elizabeth Goldbarg & Marco Goldbarg & Myriam Delgado, 2012. "An experimental analysis of evolutionary heuristics for the biobjective traveling purchaser problem," Annals of Operations Research, Springer, vol. 199(1), pages 305-341, October.
    2. I. F. C. Fernandes & E. F. G. Goldbarg & S. M. D. M. Maia & M. C. Goldbarg, 2020. "Empirical study of exact algorithms for the multi-objective spanning tree," Computational Optimization and Applications, Springer, vol. 75(2), pages 561-605, March.
    3. Andréa Santos & Diego Lima & Dario Aloise, 2014. "Modeling and solving the bi-objective minimum diameter-cost spanning tree problem," Journal of Global Optimization, Springer, vol. 60(2), pages 195-216, October.
    4. Martí, Rafael & Campos, Vicente & Resende, Mauricio G.C. & Duarte, Abraham, 2015. "Multiobjective GRASP with Path Relinking," European Journal of Operational Research, Elsevier, vol. 240(1), pages 54-71.
    5. Iago A. Carvalho & Amadeu A. Coco, 2023. "On solving bi-objective constrained minimum spanning tree problems," Journal of Global Optimization, Springer, vol. 87(1), pages 301-323, September.

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