Lipschitzian optimization without the Lipschitz constant
Journal of Optimization Theory and Applications
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
Global Optimization by Multilevel Coordinate Search
Journal of Global Optimization
A Radial Basis Function Method for Global Optimization
Journal of Global Optimization
A Taxonomy of Global Optimization Methods Based on Response Surfaces
Journal of Global Optimization
Constrained Global Optimization of Expensive Black Box Functions Using Radial Basis Functions
Journal of Global Optimization
Journal of Global Optimization
Mixture surrogate models based on Dempster-Shafer theory for global optimization problems
Journal of Global Optimization
A metamodel-assisted evolutionary algorithm for expensive optimization
Journal of Computational and Applied Mathematics
An adaptive least-squares collocation radial basis function method for the HJB equation
Journal of Global Optimization
Computers and Operations Research
Journal of Global Optimization
Global optimization of expensive black box problems with a known lower bound
Journal of Global Optimization
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Powerful response surface methods based on kriging and radial basis function (RBF) interpolation have been developed for expensive, i.e. computationally costly, global nonconvex optimization. We have implemented some of these methods in the solvers rbfSolve and EGO in the TOMLAB Optimization Environment ( http://www.tomopt.com/tomlab/ ). In this paper we study algorithms based on RBF interpolation. The practical performance of the RBF algorithm is sensitive to the initial experimental design, and to the static choice of target values. A new adaptive radial basis interpolation (ARBF) algorithm, suitable for parallel implementation, is presented. The algorithm is described in detail and its efficiency is analyzed on the standard test problem set of Dixon---Szegö. Results show that it outperforms the published results of rbfSolve and several other solvers.