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Analysis of Static Simulated Annealing Algorithms

Author

Listed:
  • J.E. Orosz

    (Wright–Patterson Air Force Base)

  • S.H. Jacobson

    (University of Illinois)

Abstract

Generalized hill climbing (GHC) algorithms provide a framework for modeling local search algorithms to address intractable discrete optimization problems. This paper introduces a measure for determining the expected number of iterations to visit a predetermined objective function level, given that an inferior objective function level has been reached in a finite number of iterations. A variation of simulated annealing (SA), termed static simulated annealing (S2A), is analyzed using this measure. S2A uses a fixed cooling schedule during the algorithm execution. Though S2A is probably nonconvergent, its finite-time performance can be assessed using the finite-time performance measure defined in this paper.

Suggested Citation

  • J.E. Orosz & S.H. Jacobson, 2002. "Analysis of Static Simulated Annealing Algorithms," Journal of Optimization Theory and Applications, Springer, vol. 115(1), pages 165-182, October.
    • Handle: RePEc:spr:joptap:v:115:y:2002:i:1:d:10.1023_a:1019633214895
    DOI: 10.1023/A:1019633214895
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    References listed on IDEAS

    as
    1. Bruce Hajek, 1988. "Cooling Schedules for Optimal Annealing," Mathematics of Operations Research, INFORMS, vol. 13(2), pages 311-329, May.
    2. Mark Fleischer & Sheldon H. Jacobson, 1999. "Information Theory and the Finite-Time Behavior of the Simulated Annealing Algorithm: Experimental Results," INFORMS Journal on Computing, INFORMS, vol. 11(1), pages 35-43, February.
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    Cited by:

    1. Marc Robini & Pierre-Jean Reissman, 2013. "From simulated annealing to stochastic continuation: a new trend in combinatorial optimization," Journal of Global Optimization, Springer, vol. 56(1), pages 185-215, May.
    2. Zahra Pooranian & Mohammad Shojafar & Jemal H. Abawajy & Ajith Abraham, 2015. "An efficient meta-heuristic algorithm for grid computing," Journal of Combinatorial Optimization, Springer, vol. 30(3), pages 413-434, October.
    3. Alexander Nikolaev & Sheldon Jacobson & Shane Hall & Darrall Henderson, 2011. "A framework for analyzing sub-optimal performance of local search algorithms," Computational Optimization and Applications, Springer, vol. 49(3), pages 407-433, July.
    4. Sheldon H. Jacobson & Shane N. Hall & Laura A. McLay & Jeffrey E. Orosz, 2005. "Performance Analysis of Cyclical Simulated Annealing Algorithms," Methodology and Computing in Applied Probability, Springer, vol. 7(2), pages 183-201, June.
    5. Franzin, Alberto & Stützle, Thomas, 2023. "A landscape-based analysis of fixed temperature and simulated annealing," European Journal of Operational Research, Elsevier, vol. 304(2), pages 395-410.
    6. Luis Rios & Nikolaos Sahinidis, 2013. "Derivative-free optimization: a review of algorithms and comparison of software implementations," Journal of Global Optimization, Springer, vol. 56(3), pages 1247-1293, July.

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