A parallel statistical cooling algorithm

  • E. H. L. Aarts
  • F. M. J. de Bont
  • J. H. A. Habers
  • P. J. M. van Laarhoven
Contributed Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 210)

Abstract

Statistical Cooling is a new optimization technique based on Monte-Carlo iterative improvement. Here we propose a parallel formulation of the statistical cooling algorithm based on the requirement that quasi-equilibrium is preserved throughout the optimization process. It is shown that the parallel algorithm can be executed in polynomial time. Performance of the algorithm is discussed by means of an implementation on an experimental multi-processor architecture. It is concluded that substantial reductions of computation time can be achieved by the parallel algorithm in comparison with the sequential algorithm.

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References

  1. [1]
    Aarts, E.H.L. and P.J.M. van Laarhoven, "Simulated Annealing: State of the Art and Current Trends", to be published.Google Scholar
  2. [2]
    Aarts, E.H.L. and P.J.M. van Laarhoven, "Statistical Cooling: A General Approach to Combinatorial Optimization Problems", Philips Journ. Res. 40(1985)193.Google Scholar
  3. [3]
    Aarts, E.H.L. and P.J.M. van Laarhoven, "Quantitative Analysis of the Statistical Cooling Algorithm", Philips Journ. Res., to be published.Google Scholar
  4. [4]
    Ackley, D.H., G.E. Hinton and T.J. Sejnowski, "A Learning Algorithm for Boltzmann Machines", Cognitive Science 9(1985)147.CrossRefGoogle Scholar
  5. [5]
    R.M. Garey and J.D. Johnson, "Computers and Intractability: A Guide to the Theory of NP-Completeness", W.H. Freeman and Co., San Francisco, 1979.Google Scholar
  6. [6]
    Geman S. and D. Geman, "Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images", IEEE Trans. Pattern Anal. Mach. Intel. 6(1984)721.Google Scholar
  7. [7]
    Kirkpatrick, S., C.D. Gelatt Jr. and M.P. Vecchi, "Optimization by Simulated Annealing", Science 220(1983)671.Google Scholar
  8. [8]
    Lin, S. and B.W. Kernighan, "An Effective Heuristic Algorithm for the Traveling Salesman Problem", Operations Res. 21(1973)498.Google Scholar
  9. [9]
    Metropolis, M. et al. "Equation of State Calculations by Fast Computing Machines", J. Chem. Phys. 21(1953)1087.Google Scholar
  10. [10]
    Romeo, F. and A.L. Sangiovanni-Vincentelli, "Probabilistic Hill Climbing Algorithms: Properties and Applications", University of Berkeley, Mem. No. UCB/ERL M 84/34, 1984.Google Scholar

Copyright information

© Springer-Verlag 1986

Authors and Affiliations

  • E. H. L. Aarts
    • 1
  • F. M. J. de Bont
    • 1
  • J. H. A. Habers
    • 1
  • P. J. M. van Laarhoven
    • 1
  1. 1.Philips Research LaboratoriesEindhoventhe Netherlands

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