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A Fast Vertex Weighting-Based Local Search for Finding Minimum Connected Dominating Sets

Author

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  • Xinyun Wu

    (School of Computer Science, Hubei University of Technology, Wuhan 430068, P.R. China)

  • Zhipeng Lü

    (SMART, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, P.R. China)

  • Fred Glover

    (Department of Electrical, Computer and Energy Engineering, College of Engineering & Applied Science, University of Colorado Boulder, Boulder, Colorado 80309)

Abstract

The minimum connected dominating set (MCDS) problem consists of selecting a minimum set of vertices from an undirected graph, such that each vertex not in this set is adjacent to at least one of the vertices in it, and the subgraph induced by this vertex set is connected. This paper presents a fast vertex weighting (FVW) algorithm for solving the MCDS problem, which integrates several distinguishing features, such as a vertex weighting-based local search with tabu and perturbation strategies to help the search to jump out of the local optima, as well as a search space reduction strategy to improve the search efficiency. Computational experiments on four sets of 112 commonly used public benchmark instances, as well as 15 newly introduced sparse instances, show that FVW is highly competitive compared with the state-of-the-art algorithms in the literature despite its simplicity. FVW improves the previous best-known results for 20 large public benchmark instances while matching the best-known results for all but 2 of the remaining ones. Several ingredients of FVW are investigated to demonstrate the importance of the proposed ideas and techniques. Summary of Contribution: As a challenging classical NP-hard problem, the minimum connected dominating set (MCDS) problem has been studied for decades in the areas of both operations research and computer science, although there does not exist an exact polynomial algorithm for solving it. Thus, the new breakthrough on this classical NP-hard problem in terms of the computational results on classical benchmark instances is significant. This paper presents a new fast vertex weighting local search for solving the MCDS problem. Computational experiments on four sets of 112 commonly used public benchmark instances show that fast vertex weighting (FVW) is able to improve the previous best-known results for 20 large instances while matching the best-known results for all but 2 of the remaining instances. Several ingredients of FVW are also investigated to demonstrate the importance of the proposed ideas and techniques.

Suggested Citation

  • Xinyun Wu & Zhipeng Lü & Fred Glover, 2022. "A Fast Vertex Weighting-Based Local Search for Finding Minimum Connected Dominating Sets," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 817-833, March.
    • Handle: RePEc:inm:orijoc:v:34:y:2022:i:2:p:817-833
    DOI: 10.1287/ijoc.2021.1106
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    References listed on IDEAS

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    1. Abilio Lucena & Nelson Maculan & Luidi Simonetti, 2010. "Reformulations and solution algorithms for the maximum leaf spanning tree problem," Computational Management Science, Springer, vol. 7(3), pages 289-311, July.
    2. Gao, Chao & Yao, Xin & Weise, Thomas & Li, Jinlong, 2015. "An efficient local search heuristic with row weighting for the unicost set covering problem," European Journal of Operational Research, Elsevier, vol. 246(3), pages 750-761.
    3. Edmund K. Burke & Yuri Bykov, 2016. "An Adaptive Flex-Deluge Approach to University Exam Timetabling," INFORMS Journal on Computing, INFORMS, vol. 28(4), pages 781-794, November.
    4. Anirudh Subramanyam & Panagiotis P. Repoussis & Chrysanthos E. Gounaris, 2020. "Robust Optimization of a Broad Class of Heterogeneous Vehicle Routing Problems Under Demand Uncertainty," INFORMS Journal on Computing, INFORMS, vol. 32(3), pages 661-681, July.
    5. Austin Buchanan & Je Sang Sung & Sergiy Butenko & Eduardo L. Pasiliao, 2015. "An Integer Programming Approach for Fault-Tolerant Connected Dominating Sets," INFORMS Journal on Computing, INFORMS, vol. 27(1), pages 178-188, February.
    6. Bernard Gendron & Abilio Lucena & Alexandre Salles da Cunha & Luidi Simonetti, 2014. "Benders Decomposition, Branch-and-Cut, and Hybrid Algorithms for the Minimum Connected Dominating Set Problem," INFORMS Journal on Computing, INFORMS, vol. 26(4), pages 645-657, November.
    7. Ruizhi Li & Shuli Hu & Huan Liu & Ruiting Li & Dantong Ouyang & Minghao Yin, 2019. "Multi-Start Local Search Algorithm for the Minimum Connected Dominating Set Problems," Mathematics, MDPI, vol. 7(12), pages 1-14, December.
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