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Convergence and first hitting time of simulated annealing algorithms for continuous global optimization

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  • M. Locatelli

Abstract

In this paper simulated annealing algorithms for continuous global optimization are considered.Under the simplifying assumption of known optimal value, the convergence of the algorithms and an upper bound for the expected first hitting time, i.e. the expected number of iterations before reaching the global optimum value within accuracy ε, are established. The obtained results are compared with those for the ideal algorithm PAS (Pure Adaptive Search) and for the simple PRS (Pure Random Search) algorithm. Copyright Springer-Verlag Berlin Heidelberg 2001

Suggested Citation

  • M. Locatelli, 2001. "Convergence and first hitting time of simulated annealing algorithms for continuous global optimization," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 54(2), pages 171-199, December.
    • Handle: RePEc:spr:mathme:v:54:y:2001:i:2:p:171-199
    DOI: 10.1007/s001860100149
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    Cited by:

    1. Rubenthaler, Sylvain & Rydén, Tobias & Wiktorsson, Magnus, 2009. "Fast simulated annealing in with an application to maximum likelihood estimation in state-space models," Stochastic Processes and their Applications, Elsevier, vol. 119(6), pages 1912-1931, June.
    2. Weitao Sun & Yuan Dong, 2011. "Study of multiscale global optimization based on parameter space partition," Journal of Global Optimization, Springer, vol. 49(1), pages 149-172, January.
    3. G. R. Wood & D. W. Bulger & W. P. Baritompa & D. L. J. Alexander, 2006. "Backtracking Adaptive Search: Distribution of Number of Iterations to Convergence," Journal of Optimization Theory and Applications, Springer, vol. 128(3), pages 547-562, March.
    4. Dawid Tarłowski, 2017. "On the convergence rate issues of general Markov search for global minimum," Journal of Global Optimization, Springer, vol. 69(4), pages 869-888, December.

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