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An adaptive radial basis algorithm (ARBF) for expensive black-box mixed-integer constrained global optimizationPrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
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PrimeFaces.cw("AccordionPanel","widget_formSmash_responsibleOrgs",{id:"formSmash:responsibleOrgs",widgetVar:"widget_formSmash_responsibleOrgs",multiple:true}); 2008 (English)In: Optimization and Engineering, ISSN 1389-4420, E-ISSN 1573-2924, Vol. 9, no 3, 311-339 p.Article in journal (Refereed) Published
##### Abstract [en]

##### Place, publisher, year, edition, pages

Springer, 2008. Vol. 9, no 3, 311-339 p.
##### Keyword [en]

Global optimization, radial basis functions, response surface model, surrogate model, expensive function, CPU-intensive, optimization software, splines, mixed-integer nonlinear programming, nonconvex, derivative-free, black-box, linear constraints, nonlinear constraints
##### National Category

Computational Mathematics
##### Identifiers

URN: urn:nbn:se:liu:diva-77076DOI: 10.1007/s11081-008-9037-3ISI: 000259577400002OAI: oai:DiVA.org:liu-77076DiVA: diva2:524839
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Available from: 2012-05-04 Created: 2012-05-04 Last updated: 2015-02-26Bibliographically approved
##### In thesis

Response surface methods based on kriging and radial basis function (RBF) interpolationhave been successfully applied to solve expensive, i.e. computationally costly,global black-box nonconvex optimization problems.In this paper we describe extensions of these methods to handlelinear, nonlinear, and integer constraints.In particular, algorithms for standard RBF and the new adaptive RBF (ARBF) aredescribed.Note, however, while the objective function may be expensive, we assumethat any nonlinear constraints are either inexpensive or are incorporatedinto the objective function via penalty terms.Test results are presented on standard test problems, both nonconvexproblems with linear and nonlinear constraints, and mixed-integernonlinear problems (MINLP). Solvers in the TOMLAB OptimizationEnvironment (http://tomopt.com/tomlab/) have been compared,specifically the three deterministic derivative-free solversrbfSolve, ARBFMIP and EGO with three derivative-based mixed-integernonlinear solvers, OQNLP, MINLPBB and MISQP, as well as the GENOsolver implementing a stochastic genetic algorithm. Results showthat the deterministic derivative-free methods compare well with thederivative-based ones, but the stochastic genetic algorithm solver isseveral orders of magnitude too slow for practical use.When the objective function for the test problems is costly to evaluate,the performance of the ARBF algorithm proves to be superior.

1. Models and Methods for Costly Global Optimization and Military Decision Support Systems$(function(){PrimeFaces.cw("OverlayPanel","overlay524846",{id:"formSmash:j_idt647:0:j_idt651",widgetVar:"overlay524846",target:"formSmash:j_idt647:0:parentLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

2. Algorithms for Costly Global Optimization$(function(){PrimeFaces.cw("OverlayPanel","overlay790556",{id:"formSmash:j_idt647:1:j_idt651",widgetVar:"overlay790556",target:"formSmash:j_idt647:1:parentLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

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