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An Adaptive Radial Basis Algorithm (ARBF) for Mixed-Integer Expensive Constrained Global Optimization
Mälardalen University, Department of Mathematics and Physics. (Applied Optimization and Modeling)
2005 (English)In: Proceedings of the International Workshop on Global Optimization / [ed] I. Garcia; L.G. Casado; E.M.T Hendrix; B. Tóth, Universidad de Almería , 2005, p. 133-140Conference paper, Published paper (Refereed)
Abstract [en]

A mixed-integer constrained extension of the radial basis function (RBF) interpolation algorithm for computationally costly global non-convex optimization is presented. Implementation in TOM-LAB (http://tomlab.biz) solver rbfSolve is discussed. The algorithm relies on mixed-integer nonlinear (MINLP) sub solvers in TOMLAB, e.g. OQNLP, MINLPBB or the constrained DIRECT solvers (glcDirect or glcSolve). Depending on the initial experimental design, the basic RBF algorithm sometimes fails and make no progress. A new method how to detect when there is a problem is presented. We discuss the causes and present a new faster and more robust Adaptive RBF (ARBF) algorithm. Test results for unconstrained problems are discussed.

Place, publisher, year, edition, pages
Universidad de Almería , 2005. p. 133-140
Keywords [en]
Expensive, global, mixed-integer, nonconvex, optimization, software, black box
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:mdh:diva-3143OAI: oai:DiVA.org:mdh-3143DiVA, id: diva2:115807
Conference
The International Workshop on Global Optimization, San Jose, Spain, September 18-22, 2005
Available from: 2008-03-28 Created: 2008-03-28 Last updated: 2022-10-14Bibliographically approved

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Holmström, Kenneth

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