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Cooperative Parallel Variable Neighborhood Search for the p-Median

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Abstract

We propose a cooperative multi-search method for the Variable Neighborhood Search (VNS) meta-heuristic based on the central-memory mechanism that has been successfully applied to a number of difficult combinatorial problems. In this approach, several independent VNS meta-heuristics cooperate by asynchronously exchanging information about the best solutions identified so far, thus conserving the simplicity of the original, sequential VNS ideas. The p-median problem (PM) serves as test case. Extensive experimentations have been conducted on the classical TSPLIB benchmark problem instances with up to 11948 customers and 1000 medians, without any particular calibration of the parallel method. The results indicate that, compared to sequential VNS, the cooperative strategy yields significant gains in terms of computation time without a loss in solution quality.

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Authors and Affiliations

  1. Departement management et technologie, Université du Québec à Montréal and Centre de recherche sur les transports, Université de Montréal, Canada

    Teodor Gabriel Crainic

  2. Dept. d'informatique et recherche opérationnelle and Centre de recherche sur les transports, Université de Montréal, Canada

    Michel Gendreau

  3. GERAD and HEC Montréal, Canada

    Pierre Hansen

  4. GERAD and Matematički Institut, SANU, Belgrade, Yugoslavia

    Nenad Mladenović

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  1. Teodor Gabriel Crainic
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  2. Michel Gendreau
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  4. Nenad Mladenović
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Correspondence to Teodor Gabriel Crainic.

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Crainic, T.G., Gendreau, M., Hansen, P. et al. Cooperative Parallel Variable Neighborhood Search for the p-Median. Journal of Heuristics 10, 293–314 (2004). https://doi.org/10.1023/B:HEUR.0000026897.40171.1a

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