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An Efficient Simple Cooling Schedule for Simulated Annealing

  • Mir M. Atiqullah
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3045)

Abstract

The capability of Global solution of an optimization problem is the forte of Simulated Annealing (SA). Theoretically only the infinite-time algorithm can guarantee the global solution. The finite-time characteristics of the algorithm depend largely on the ensemble of certain control parameters. Since the main parameter is dubbed temperature, the dynamics of how it is manipulated is widely known as cooling schedule. A variety of methods, from simple geometric to highly complex, have been proposed in the literature. While global solution capability has been the overall goal for all implementation, few schedules combined effective solution with simplicity of the cooling schedule. A novel schedule is proposed which combines efficiency with simplicity into an easily implementable algorithm. Several fundamental cooling schemes are compared with the proposed one based on 2 test problems. Our schedule faired competitively with most while being the simplest. Keywords: Optimization, simulated annealing, cooling schedule.

Keywords

Optimization simulated annealing cooling schedule 

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References

  1. 1.
    Kirkpatrick, S., Gelatt Jr., C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220, 671–680 (1983)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Aarts, E.H.L., van Laarhoven, P.J.M.: Statistical Cooling: A General Approach to Combinatorial Optimization Problems. Philips J. of Research 40, 193–226 (1985)Google Scholar
  3. 3.
    Atiqullah, Mir, Rao, S.S.: Tuned Annealing for Optimization. In: Proceedings of the 2001 International Conference of Computational Science (ICCS 2001), San Francisco, CA, May 28-30 (2001)Google Scholar
  4. 4.
    Rao, S.S.: Engineering Optimization: Theory and practice. Wiley, Chichester (1995)Google Scholar
  5. 5.
    Huang, M.D., Romeo, F., Sangiovanni-Vincentelli, A.L.: An Efficient General Cooling Schedule for Simulated Annealing. In: Proceedings of IEEE International Conference on Computer-Aided Design, Santa Clara, November 1986, pp. 381–384 (1986)Google Scholar
  6. 6.
    Otten, R.H.J.M., van Ginneken, L.P.P.P.: Floorplan design using simulated annealing. In: Proceding of the IEEE International Conference on Computer Aided Design, Santa Clara, November 1984, pp. 96–98 (1984)Google Scholar
  7. 7.
    Romeo, F., Sangiovanni-Vincentelli, A.L.: Probabilistic Hill Climbing Algorithms: Properties and Applications. In: Proceedings of the Chapel Hill Conference and VL SIGoogle Scholar
  8. 8.
    Bennage, W.A., Dhingra, A.K.: Optimization of Truss Topology Using Tabu Search. International Journal for Numerical Methods in Engineering 38, 4035–4052 (1995)zbMATHCrossRefGoogle Scholar
  9. 9.
    Rajeev, S., Krishnamurty, C.S.: Discrete Optimization of Structures using Genetic Algorithms. Journal of Struct. Engineering 118(5), 1233–1250 (1992)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Mir M. Atiqullah
    • 1
  1. 1.Aerospace and Mechanical Engineering Department Parks College of Engineering and AviationSaint Louis UniversitySaint LouisUSA

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