期刊
OPTIMIZATION AND ENGINEERING
卷 11, 期 4, 页码 501-532出版社
SPRINGER
DOI: 10.1007/s11081-009-9087-1
关键词
Simulation based optimization; Radial basis functions; Multiobjective; Noise; Response surface; Surrogate model; Black box function
类别
资金
- Swedish Foundation for Strategic Research
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.
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