• Louis G. Birta
  • Gilbert Arbez
Part of the Simulation Foundations, Methods and Applications book series (SFMA)


The goal of a simulation project is often formulated in terms of an optimization task, and this chapter explores this topic within the CTDS context. A key facet of this task is the identification of a criterion function that measures some aspect of the SUI’s behaviour that is related to the project goal(s). The criterion function is dependent on some set of parameters embedded within the SUI. The optimization task corresponds to finding a ‘best value’ for this set of parameters as indicated by an extreme value (either maximum or minimum) for the selected criterion function. This problem of extremizing the value of a criterion function by locating a best value for a set of parameters has been widely studied in the optimization literature. In the modelling and simulation context, the problem is distinctive inasmuch as the evaluation of the criterion function is linked to a simulation model. Several well-established numerical procedures that can be directly applied when the simulation model falls in the CTDS category are outlined in this chapter. Included here are both a gradient-independent method (the Nelder–Mead Simplex method) and a gradient-dependent method (the conjugate gradient method). Associated issues of gradient evaluation and the linear search problem are discussed.


Optimal Control Problem Search Direction Line Search Conjugate Gradient Method Criterion Function 
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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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Louis G. Birta
    • 1
  • Gilbert Arbez
    • 1
  1. 1.School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada

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