Parameter Optimization of Complex Simulation Models

  • Elisabeth Syrjakow
  • Michael Syrjakow


Today, a great shortcoming of most of the available simulation tools is, that optimization is not sufficiently supported by appropriate optimization techniques. This situation is mainly caused by the lack of qualified strategies for optimization of simulation models. Traditional indirect optimization techniques based on exploitation of analytical information like gradients etc. cannot be applied, because only goal function values are available which have to be calculated by an often very expensive simulation process. In the first part of this paper, some powerful direct optimization algorithms are presented which work iteratively, only requiring goal function values. These strategies are combinations of direct global and local optimization methods (Genetic Algorithms, Simulated Annealing, Hill-Climbing, etc.) trying to merge the advantages of global and local search. Our developed strategies have been implemented and integrated into REMO (REsearch Model Optimization package) representing a software tool for experimentation with simulation models. At the moment REMO is extended and completely reimplemented in Java to make it available on the World Wide Web. The ongoing development process of REMO is described in the second part of this paper.


Local Search Optimization Strategy Optimum Point Goal Function Global Optimization Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. [1]
    Aarts, E.; Korst, J: Simulated Annealing and Boltzmann Machines: Wiley 1990.Google Scholar
  2. [2]
    Glover, F; Laguna, M: Tabu Search: Kluwer Academic Pub. 1997.Google Scholar
  3. [3]
    Goldberg, D.E.: Genetic Algorithms in Search: Optimization and Machine Learning: Addison-Wesley 1989.zbMATHGoogle Scholar
  4. [4]
    Gramlich, J.: Optimierung stochastisch verrauschter Zielfunktionen: Diplomarbeit am Institut für Rechnerentwurf und Fehlertoleranz. Universität Karlsruhe 1996.Google Scholar
  5. [5]
    Hooke, R.A.; Jeeves, T.A.: Direct Search Solution for Numerical and Statistical Problems. Journal ACM 8 (1961), pp. 212/221.Google Scholar
  6. [6]
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs: Springer 1992.Google Scholar
  7. [7]
    Orfali, R.; Harkey, D.: Client/Server Programming with JAVA and CORBA: John Wiley Sons Inc. 1998.Google Scholar
  8. [8]
    Schwefel, H.-P.: Numerical Optimization of Computer Models: Wiley 1981.Google Scholar
  9. [9]
    Sommer; T: CORBA-basierte Parameteroptimierung von Simulationsmodellen in verteilter Umgebung: Diplomarbeit am Institut für Rechnerentwurf und Fehlertoleranz. Universität Karlsruhe 1999.Google Scholar
  10. [10]
    Syrjakow, M.: Verfahren zur effizienten Parameteroptimierung von Simulationsmodellen: Dissertation am Institut für Rechnerentwurf und Fehlertoleranz der Universität Karlsruhe: Berichte aus der Informatik. Shaker Verlag 1997.Google Scholar
  11. [11]
    Syrjakow, M; Szczerbicka, H.: REMO - REsearch Model Optimization Package: Tool Descriptions from the 9th International Conference on Modelling Techniques and Tools for Computer Performance Evaluation (Performance Tools’97) and the 7th International Workshop on Petri Nets and Performance Models (PNPM’97). Saint-Malo, France, June 3–6, 1997, pp. 20/22.Google Scholar
  12. [12]
    Syrjakow, M; Szczerbicka, H.: Efficient Parameter Optimization based on Combination of Direct Global and Local Search Methods: in Evolutionary Algorithms (IMA Volumes in Mathematics and Its Applications, Vol. 111), L.D. Davis, K. De Jong, M.D. Vose, L.D. Whitley (eds.), Springer Verlag New York, 1999, pp. 227/249.Google Scholar
  13. [13]
    Syrjakow, M; Szczerbicka, K; Berthold, M.R.; Huber, K.-P.: Acceleration of Direct Model Optimization Methods by Function Approximation: Proceedings of the 8th European Simulation Symposium (ESS’96).Genoa, Italy, October 24–26, 1996, Volume II, pp. 181/186.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2001

Authors and Affiliations

  • Elisabeth Syrjakow
    • 1
  • Michael Syrjakow
    • 1
  1. 1.Institute for Computer Design and Fault Tolerance (Prof D. Schmid)University of KarlsruheKarlsruheGermany

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