A method for simulation based optimization using radial basis functions
Artikel i vetenskaplig tidskrift, 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.

Response surface

Surrogate model

Simulation based optimization

Noise

Black box function

Multiobjective

Radial basis functions

Författare

Stefan Jakobsson

Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik

Michael Patriksson

Chalmers, Matematiska vetenskaper, Matematik

Göteborgs universitet

Johan Rudholm

Chalmers

Göteborgs universitet

Adam Wojciechowski

Chalmers, Matematiska vetenskaper, Matematik

Göteborgs universitet

Optimization & Engineering

1573-2924 (eISSN)

Vol. 11 4 501-532

Ämneskategorier

Beräkningsmatematik

DOI

10.1007/s11081-009-9087-1

Mer information

Senast uppdaterat

2018-09-10