A method for simulation based optimization using radial basis functions
Journal article, 2010

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.

Black box function

Surrogate model

Simulation based optimization


Response surface

Radial basis functions



Stefan Jakobsson

Fraunhofer-Chalmers Centre

Michael Patriksson

University of Gothenburg

Chalmers, Mathematical Sciences, Mathematics

Johan Rudholm

Chalmers, Mathematical Sciences, Mathematics

University of Gothenburg

Adam Wojciechowski

Chalmers, Mathematical Sciences, Mathematics

University of Gothenburg

Optimization & Engineering

1573-2924 (eISSN)

Vol. 11 4 501-532

Subject Categories

Computational Mathematics



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