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Exploiting run time distributions to compare sequential and parallel stochastic local search algorithms

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Abstract

Run time distributions or time-to-target plots are very useful tools to characterize the running times of stochastic algorithms for combinatorial optimization. We further explore run time distributions and describe a new tool to compare two algorithms based on stochastic local search. For the case where the running times of both algorithms fit exponential distributions, we derive a closed form index that gives the probability that one of them finds a solution at least as good as a given target value in a smaller computation time than the other. This result is extended to the case of general run time distributions and a numerical iterative procedure is described for the computation of the above probability value. Numerical examples illustrate the application of this tool in the comparison of different sequential and parallel algorithms for a number of distinct problems.

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Authors and Affiliations

  1. Department of Computer Science, Universidade Federal Fluminense, Rua Passo da Pátria 156, Niterói, RJ, 24210-240, Brazil

    Celso C. Ribeiro & Isabel Rosseti

  2. Department of Electronic Engineering, Universidad Técnica Federico Santa María, Telematics Group, Av. España 1680, Valparaíso, Chile

    Reinaldo Vallejos

Authors
  1. Celso C. Ribeiro
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  2. Isabel Rosseti
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  3. Reinaldo Vallejos
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Correspondence to Celso C. Ribeiro.

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Ribeiro, C.C., Rosseti, I. & Vallejos, R. Exploiting run time distributions to compare sequential and parallel stochastic local search algorithms. J Glob Optim 54, 405–429 (2012). https://doi.org/10.1007/s10898-011-9769-z

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  • DOI: https://doi.org/10.1007/s10898-011-9769-z

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