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Combining Edge Weight and Vertex Weight for Minimum Vertex Cover Problem

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Frontiers in Algorithmics (FAW 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8497))

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Abstract

The Minimum Vertex Cover (MVC) problem is an important NP-hard combinatorial optimization problem. Constraint weighting is an effective technique in stochastic local search algorithms for the MVC problem. The edge weight and the vertex weight have been used separately by different algorithms. We present a new local search algorithm, namely VEWLS, which integrates the edge weighting scheme with the vertex weighting scheme. To the best of our knowledge, it is the first time to combine two weighting schemes for the MVC problem. Experiments over both the DIMACS benchmark and the BHOSLIB benchmark show that VEWLS outperforms NuMVC, the state-of-the-art local search algorithm for MVC, on 73% and 68% of the instances, respectively.

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Authors and Affiliations

  1. State Key Lab. of Software Development Environment, Beihang University, Beijing, 100191, China

    Zhiwen Fang, Yang Chu, Xu Feng & Ke Xu

  2. Department of Computer Science, Illinois Institute of Technology, Chicago, 60616, USA

    Kan Qiao

Authors
  1. Zhiwen Fang
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  2. Yang Chu
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  3. Kan Qiao
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  4. Xu Feng
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  5. Ke Xu
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Texas A&M University, USA

    Jianer Chen

  2. Cornell University, USA

    John E. Hopcroft

  3. School of Information Science and Engineering, Central South University, 410083, Changsha, China

    Jianxin Wang

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© 2014 Springer International Publishing Switzerland

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Fang, Z., Chu, Y., Qiao, K., Feng, X., Xu, K. (2014). Combining Edge Weight and Vertex Weight for Minimum Vertex Cover Problem. In: Chen, J., Hopcroft, J.E., Wang, J. (eds) Frontiers in Algorithmics. FAW 2014. Lecture Notes in Computer Science, vol 8497. Springer, Cham. https://doi.org/10.1007/978-3-319-08016-1_7

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  • DOI: https://doi.org/10.1007/978-3-319-08016-1_7

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08015-4

  • Online ISBN: 978-3-319-08016-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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