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An adaptive radial basis algorithm (ARBF) for expensive black-box global optimization
Mälardalen University, Department of Mathematics and Physics. (Applied Optimization and Modeling)
2008 (English)In: Journal of Global Optimization, ISSN 1573-2916, Vol. 41, no 3, p. 447-464Article in journal (Refereed) Published
Abstract [en]

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-Szego. Results show that it outperforms the published results of rbfSolve and several other solvers.

Place, publisher, year, edition, pages
2008. Vol. 41, no 3, p. 447-464
Keywords [en]
Mathematical Programming, Nonlinear Programming, Operations Research, MATLAB, CUTE, AMPL, Graphical User Interface, Software Engineering, Mathematical Software, Optimization, Algorithms, Nonlinear Least Squares
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:mdh:diva-3079DOI: 10.1007/s10898-007-9256-8ISI: 000257489700009Scopus ID: 2-s2.0-47249095410OAI: oai:DiVA.org:mdh-3079DiVA, id: diva2:115743
Available from: 2008-03-28 Created: 2008-03-28 Last updated: 2013-02-18Bibliographically approved

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