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Efficient solution of many instances of a simulation-based optimization problem utilizing a partition of the decision space

Journal article
Authors Zuzana Nedelkova
Peter Lindroth
Michael Patriksson
Ann-Brith Strömberg
Published in Annals of Operations Research
Volume 265
Issue 1
Pages 93–118
ISSN 0254-5330
Publication year 2018
Published at Department of Mathematical Sciences
Pages 93–118
Language en
Links https://doi.org/10.1007/s10479-017-...
Subject categories Optimization, systems theory, Transport Systems and Logistics

Abstract

This paper concerns the solution of a class of mathematical optimization problems with simulation-based objective functions. The decision variables are partitioned into two groups, referred to as variables and parameters, respectively, such that the objective function value is influenced more by the variables than by the parameters. We aim to solve this optimization problem for a large number of parameter settings in a computationally efficient way. The algorithm developed uses surrogate models of the objective function for a selection of parameter settings, for each of which it computes an approximately optimal solution over the domain of the variables. Then, approximate optimal solutions for other parameter settings are computed through a weighting of the surrogate models without requiring additional expensive function evaluations. We have tested the algorithm's performance on a set of global optimization problems differing with respect to both mathematical properties and numbers of variables and parameters. Our results show that it outperforms a standard and often applied approach based on a surrogate model of the objective function over the complete space of variables and parameters.

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Denna text är utskriven från följande webbsida:
http://www.gu.se/english/research/publication/?publicationId=261063
Utskriftsdatum: 2019-11-21