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The influence of Experimental Designs on the Performance of Surrogate Model Based Costly Global Optimization Solvers
Mälardalens högskola, Institutionen för matematik och fysik. (Applied Optimization)
Mälardalens högskola, Akademin för utbildning, kultur och kommunikation. (Applied Optimization)
2009 (engelsk)Inngår i: Studies in Informatics and Control, ISSN 1220-1766, E-ISSN 1841-429X, Vol. 18, nr 1, s. 87-95Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

When dealing with costly objective functions in optimization, one good alternative is to use a surrogate model approach. A common feature for all such methods is the need of an initial set of points, or "experimental design", in order to start the algorithm. Since the behavior of the algorithms often depends heavily on this set, the question is how to choose a good experimental design. We investigate this by solving a number of problems using different designs, and compare the outcome with respect to function evaluations and a root mean square error test of the true function versus the surrogate model produced. Each combination of problem and design is solved by 3 different solvers available in the TOMLAB optimization environment. Results indicate two designs as superior.

sted, utgiver, år, opplag, sider
National Institute for Research & Development in Informatics , 2009. Vol. 18, nr 1, s. 87-95
Emneord [en]
Black-box, Surrogate model, Costly functions, Latin Hypercube Designs, Experimental Design
HSV kategori
Identifikatorer
URN: urn:nbn:se:mdh:diva-5332ISI: 000269029600010OAI: oai:DiVA.org:mdh-5332DiVA, id: diva2:160459
Tilgjengelig fra: 2009-02-13 Laget: 2009-02-13 Sist oppdatert: 2017-12-14bibliografisk kontrollert
Inngår i avhandling
1. Algorithms for Costly Global Optimization
Åpne denne publikasjonen i ny fane eller vindu >>Algorithms for Costly Global Optimization
2009 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
Abstract [en]

There exists many applications with so-called costly problems, which means that the objective function you want to maximize or minimize cannot be described using standard functions and expressions. Instead one considers these objective functions as ``black box'' where the parameter values are sent in and a function value is returned. This implies in particular that no derivative information is available.The reason for describing these problems as expensive is that it may take a long time to calculate a single function value. The black box could, for example, solve a large system of differential equations or carrying out a heavy simulation, which can take anywhere from several minutes to several hours!These very special conditions therefore requires customized algorithms. Common optimization algorithms are based on calculating function values every now and then, which usually can be done instantly. But with an expensive problem, it may take several hours to compute a single function value. Our main objective is therefore to create algorithms that exploit all available information to the limit before a new function value is calculated. Or in other words, we want to find the optimal solution using as few function evaluations as possible.A good example of real life applications comes from the automotive industry, where on the development of new engines utilize advanced models that are governed by a dozen key parameters. The goal is to optimize the model by changing the parameters in such a way that the engine becomes as energy efficient as possible, but still meets all sorts of demands on strength and external constraints.

sted, utgiver, år, opplag, sider
Västerås: Mälardalens högskola, 2009
Serie
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 105
HSV kategori
Forskningsprogram
Matematik/tillämpad matematik
Identifikatorer
urn:nbn:se:mdh:diva-5970 (URN)978-91-86135-29-4 (ISBN)
Presentation
2009-09-03, Gamma, Hus U, Högskoleplan 1, Mälardalens Högskola, Västerås, 13:15 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2009-05-26 Laget: 2009-05-26 Sist oppdatert: 2009-08-20bibliografisk kontrollert

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