Optimization and Engineering

, Volume 11, Issue 4, pp 501–532 | Cite as

A method for simulation based optimization using radial basis functions

  • Stefan Jakobsson
  • Michael Patriksson
  • Johan Rudholm
  • Adam WojciechowskiEmail author


We propose an algorithm for the global optimization of expensive and noisy black box functions using a surrogate model based on radial basis functions (RBFs). A method for RBF-based approximation is introduced in order to handle noise. New points are selected to minimize the total model uncertainty weighted against the surrogate function value. The algorithm is extended to multiple objective functions by instead weighting against the distance to the surrogate Pareto front; it therefore constitutes the first algorithm for expensive, noisy and multiobjective problems in the literature. Numerical results on analytical test functions show promise in comparison to other (commercial) algorithms, as well as results from a simulation based optimization problem.


Simulation based optimization Radial basis functions Multiobjective Noise Response surface Surrogate model Black box function 


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Stefan Jakobsson
    • 1
  • Michael Patriksson
    • 2
    • 3
  • Johan Rudholm
    • 2
    • 3
  • Adam Wojciechowski
    • 2
    • 3
    Email author
  1. 1.Fraunhofer–Chalmers Research Centre for Industrial MathematicsGothenburgSweden
  2. 2.Department of Mathematical SciencesChalmers University of TechnologyGothenburgSweden
  3. 3.Department of Mathematical SciencesUniversity of GothenburgGothenburgSweden

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