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A hybrid metaheuristic algorithm for generalized vertex cover problem

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Abstract

The generalized vertex cover problem (GVCP) extends classic vertex cover problems to take both vertex and edge weights into consideration in the objective function. The GVCP consists in finding a vertex subset such that the sum of vertex weights together with all the corresponding edge weights is minimized. In this paper, we proposed a hybrid metaheuristic algorithm to solve GVCP (MAGVCP for short) that is based on evolutionary search and iterated neighborhood search. The algorithm uses population initializing procedure to produce high quality solutions, applies a dedicated crossover to generate offspring solutions, and finally utilizes an iterated best chosen neighborhood search to find better solutions. Experiments carried on random instances and DIMACS instances demonstrate the effectiveness of the proposed algorithm.

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Notes

  1. ftp://dimacs.rutgers.edu/pub/challenges.

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Acknowledgements

The authors of this paper wish to extend their sincere gratitude to all anonymous reviewers for their efforts. This work was supported in part by NSFC (under Grant Nos. 61370156, 61503074, 61403076, 61402070, and 61403077), the Program for New Century Excellent Talents in University (NCET-13-0724), the Youth Foundation of Northeast Normal University (1205098).

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Authors and Affiliations

  1. School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130024, China

    Shuli Hu, Ruizhi Li, Peng Zhao & Minghao Yin

  2. Key Laboratory of Applied Statistics of MOE, Northeast Normal University, Changchun, 130024, China

    Minghao Yin

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  1. Shuli Hu
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  2. Ruizhi Li
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  3. Peng Zhao
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  4. Minghao Yin
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Correspondence to Minghao Yin.

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Hu, S., Li, R., Zhao, P. et al. A hybrid metaheuristic algorithm for generalized vertex cover problem. Memetic Comp. 10, 165–176 (2018). https://doi.org/10.1007/s12293-016-0216-z

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