Global Optimization for Noisy Expensive Black-Box Multi-Modal Functions Via Radial Basis Function Surrogate

This study proposes a new surrogate global optimization algorithm that solves problems with expensive black-box multi-modal objective functions subject to homogeneous evaluation noise. Specifically, we propose a new radial basis function (RBF) surrogate to approximate noisy functions and extend the...

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Bibliographic Details
Published inProceedings - Winter Simulation Conference pp. 3020 - 3031
Main Authors Shen, Yichi, Shoemaker, Christine A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 14.12.2020
Subjects
Approximation algorithms
Fitting
Inference algorithms
Noise measurement
Optimization
Reliability
Response surface methodology
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Summary:This study proposes a new surrogate global optimization algorithm that solves problems with expensive black-box multi-modal objective functions subject to homogeneous evaluation noise. Specifically, we propose a new radial basis function (RBF) surrogate to approximate noisy functions and extend the Stochastic Response Surface method, which was developed for deterministic problems, to optimize noisy functions. Instead of conducting multiple replications at each point to mitigate the influence of noise, we only do a single observation at every sampled point and regularize the RBF surrogate by penalizing the bumpiness. The proposed algorithm sequentially identifies a new point for the expensive function evaluation from a set of randomly generated candidate points using both exploitation and exploration. Numerical studies show that the proposed noisy RBF surrogate can produce reliable approximations for noisy functions, and the proposed algorithm is effective and competitive in solving the global optimization problems with noisy evaluations.
ISSN:1558-4305
DOI:10.1109/WSC48552.2020.9384132