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Algorithms for Costly Global Optimization
Mälardalen University, School of Education, Culture and Communication.
2009 (English)Licentiate thesis, comprehensive summary (Other academic)
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.

Place, publisher, year, edition, pages
Västerås: Mälardalens högskola , 2009.
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 105
National Category
Computational Mathematics
Research subject
Mathematics/Applied Mathematics
Identifiers
URN: urn:nbn:se:mdh:diva-5970ISBN: 978-91-86135-29-4 (print)OAI: oai:DiVA.org:mdh-5970DiVA, id: diva2:219073
Presentation
2009-09-03, Gamma, Hus U, Högskoleplan 1, Mälardalens Högskola, Västerås, 13:15 (English)
Opponent
Supervisors
Available from: 2009-05-26 Created: 2009-05-26 Last updated: 2009-08-20Bibliographically approved
List of papers
1. Adaptive Radial Basis Algorithms (ARBF) for Expensive Black-Box Global MINLP Optimization
Open this publication in new window or tab >>Adaptive Radial Basis Algorithms (ARBF) for Expensive Black-Box Global MINLP Optimization
2008 (English)In: SIOPT08 - SIAM Optimization, Boston, USA, 2008Conference paper, Published paper (Refereed)
Abstract [en]

Parallel implementations of the adaptive radial basis function algorithm (ARBF)for computationally costly optimization are presented.Modifications of  ARBF to improve robustness and speed are discussed.The algoritm is implemented in solver PARFMIP in the TOMLAB Optimization Environment (http://tomopt.com/). Solvers in TOMLAB are used to solve global and local subproblems.Results and comparisons with other solvers are presentedfor a large set of global optimization test problems.Performance on some costly real-life applications are reported.

Identifiers
urn:nbn:se:mdh:diva-5343 (URN)
Available from: 2009-02-13 Created: 2009-02-13
2. The influence of Experimental Designs on the Performance of Surrogate Model Based Costly Global Optimization Solvers
Open this publication in new window or tab >>The influence of Experimental Designs on the Performance of Surrogate Model Based Costly Global Optimization Solvers
2009 (English)In: Studies in Informatics and Control, ISSN 1220-1766, E-ISSN 1841-429X, Vol. 18, no 1, p. 87-95Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
National Institute for Research & Development in Informatics, 2009
Keywords
Black-box, Surrogate model, Costly functions, Latin Hypercube Designs, Experimental Design
National Category
Social Sciences
Identifiers
urn:nbn:se:mdh:diva-5332 (URN)000269029600010 ()
Available from: 2009-02-13 Created: 2009-02-13 Last updated: 2017-12-14Bibliographically approved
3. Implementation of a One-Stage Efficient Global Optimization (EGO) Algorithm
Open this publication in new window or tab >>Implementation of a One-Stage Efficient Global Optimization (EGO) Algorithm
2009 (English)Report (Other academic)
Abstract [en]

Almost every Costly Global Optimization (CGO) solver utilizes a surrogate model, or response surface, to approximate the true (costly) function. The EGO algorithm introduced by Jones et al. utilizes the DACE framework to build an approximating surrogate model. By optimizing a less costly utility function, the algorithm determines a new point where the original objective function is evaluated. This is repeated until some convergence criteria is fulfilled.The original EGO algorithm finds the new point to sample in a two-stage process. In its first stage, the estimates of the interpolation parameters are optimized with respect to already sampled points. In the second stage, these estimated values are considered true in order to optimize the location of the new point. The use of estimate values as correct introduces a source of error.Instead, in the One-stage EGO algorithm, both parameter values and the location of a new point are optimized at the same time, removing the source of error. This new subproblem becomes more difficult, but eliminates the need of solving two subproblems.Difficulties in implementing a fast and robust One-Stage EGO algorithm in TOMLAB are discussed, especially the solution of the new subproblem.

Place, publisher, year, edition, pages
Västerås: , 2009. p. 26
Series
Research Reports MDH/UKK, ISSN 1404-4978 ; 2009-2
Keywords
Global Optimization, Costly, Expensive, EGO, Surrogate modeling
National Category
Computational Mathematics
Research subject
Mathematics/Applied Mathematics
Identifiers
urn:nbn:se:mdh:diva-5969 (URN)
Available from: 2009-05-26 Created: 2009-05-26 Last updated: 2014-02-04Bibliographically approved

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