Constrained Global Optimization of Expensive Black Box Functions Using Radial Basis Functions

By Rommel G. Regis, Christine Annette Shoemaker. Journal of Global Optimization, 2005

356 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/7b85f3bd9ed2120d92511c093ff1f57c46f7b127

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Citations in the corpus

Crashworthiness optimization of foam-filled tapered thin-walled structure using multiple surrogate models (2013, background): “1989), support vector regression (SVR) (Smola and Scholkopf 2004), and radial basis function (RBF) (Gutmann 2001; Sun et al. 2010c, 2011; Regis and Shoemaker 2005; Mullur and Messac 2006; Zhao and Xue 2010) are considered herein to approximate functional responses.”
A sequential surrogate method for reliability analysis based on radial basis function (2017, methodology): “In this paper, an efficient reliability analysis method named SSRM based on RBF is proposed, which uses the probability density function and the sample density information to update the accuracy of the important and less important region by the sequential surrogate model.”
A review of recent advances in global optimization (2009, background): “Regis and Shoemaker (2005) proposed strategies that improve the performance of the radial basis function method of Gutmann (2001).”
Design of computer experiments: A review (2017, background): “Thus, they are different from adaptive sampling techniques for surrogate assisted optimization [14, 35, 84, 89, 90, 131, 134, 135, 169].”
Survey of multifidelity methods in uncertainty propagation, inference, and optimization (2018, methodology): “A quick hyperparameter estimation is of particular importance in optimization, where the radial-basis model is updated in each iteration of the optimization process [1, 174, 175].”
Derivative-free optimization: a review of algorithms and comparison of software implementations (2012, background): “Various strategies that rely on radial basis functions have been proposed and analyzed [63,103,118], as well as extended to constrained optimization [117].”
Infill sampling criteria for surrogate-based optimization with constraint handling (2012, methodology): “A proof of concept is presented by Schonlau (1997). For global optimization, using the expected improvement is advantageous since it is likely to be larger at undersampled regions near to the global optimum, offering elements of both exploitation and exploration of the surrogate model.”
Surrogate-based methods for black-box optimization (2017, methodology): “The iterates generated by the algorithm are dense, therefore the method are able to ﬁnd global minimizers [44].”
Global optimization of expensive black box functions using potential Lipschitz constants and response surfaces (2015, methodology): “Theproposed algorithm is compared against two metamodel-based algorithms [the hybrid adaptive metamodeling algorithm (HAM) [37] and the constrained optimization using response surfaces (CORS) [35]] and the DIRECT algorithm [5] on the total 53 test functions.”
A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design (2017, methodology): “Distance based Constrained optimization using response surfaces (Regis and Shoemaker 2005) RBF for global optimization (Gutmann 2001) Stochastic response surface (Regis and Shoemaker 2007) Model-independent; global model; minimize prediction response to do local exploitation while maximize a distanc”