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Empirical study of exact algorithms for the multi-objective spanning tree

Author

Listed:
  • I. F. C. Fernandes

    (Universidade Federal do Rio Grande do Norte)

  • E. F. G. Goldbarg

    (Universidade Federal do Rio Grande do Norte)

  • S. M. D. M. Maia

    (Universidade Federal do Rio Grande do Norte)

  • M. C. Goldbarg

    (Universidade Federal do Rio Grande do Norte)

Abstract

The multi-objective spanning tree (MoST) is an extension of the minimum spanning tree problem (MST) that, as well as its single-objective counterpart, arises in several practical applications. However, unlike the MST, for which there are polynomial-time algorithms that solve it, the MoST is NP-hard. Several researchers proposed techniques to solve the MoST, each of those methods with specific potentialities and limitations. In this study, we examine those methods and divide them into two categories regarding their outcomes: Pareto optimal sets and Pareto optimal fronts. To compare the techniques from the two groups, we investigated their behavior on 2, 3 and 4-objective instances from different classes. We report the results of a computational experiment on 8100 complete and grid graphs in which we analyze specific features of each algorithm as well as the computational effort required to solve the instances.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:coopap:v:75:y:2020:i:2:d:10.1007_s10589-019-00154-1
    DOI: 10.1007/s10589-019-00154-1
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    References listed on IDEAS

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    Cited by:

    1. 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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