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Restart strategies for GRASP with path-relinking heuristics

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Abstract

GRASP with path-relinking is a hybrid metaheuristic, or stochastic local search (Monte Carlo) method, for combinatorial optimization. A restart strategy in GRASP with path-relinking heuristics is a set of iterations {i 1, i 2, …} on which the heuristic is restarted from scratch using a new seed for the random number generator. Restart strategies have been shown to speed up stochastic local search algorithms. In this paper, we propose a new restart strategy for GRASP with path-relinking heuristics. We illustrate the speedup obtained with our restart strategy on GRASP with path-relinking heuristics for the maximum cut problem, the maximum weighted satisfiability problem, and the private virtual circuit routing problem.

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Authors and Affiliations

  1. AT&T Labs Research, Florham Park, NJ, 07932-0971, USA

    Mauricio G. C. Resende

  2. Universidade Federal Fluminense, Niterói, RJ, 24210-240, Brazil

    Celso C. Ribeiro

Authors
  1. Mauricio G. C. Resende
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  2. Celso C. Ribeiro
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Correspondence to Mauricio G. C. Resende.

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Resende, M.G.C., Ribeiro, C.C. Restart strategies for GRASP with path-relinking heuristics. Optim Lett 5, 467–478 (2011). https://doi.org/10.1007/s11590-011-0322-2

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  • DOI: https://doi.org/10.1007/s11590-011-0322-2

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