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.
Authors and Affilations
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Stefan Jakobsson, Chalmers University of Technology
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Michael Patriksson, Chalmers University of Technology
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Johan Rudholm, Chalmers University of Technology
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Adam Wojciechowski, Chalmers University of Technology