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A splitting algorithm for simulation-based optimization problems with categorical variables

Journal article
Authors Zuzana Nedelkova
Christoffer Cromvik
Peter Lindroth
Michael Patriksson
Ann-Brith Strömberg
Published in Engineering optimization
Volume 51
Issue 5
Pages 815-831
ISSN 0305-215X
Publication year 2019
Published at Department of Mathematical Sciences
Pages 815-831
Language en
Keywords Design optimization, simulation-based optimization, splitting, categorical variables, tyres
Subject categories Computational Mathematics


In the design of complex products, some product components can only be chosen from a finite set of options. Each option then corresponds to a multidimensional point representing the specifications of the chosen components. A splitting algorithm that explores the resulting discrete search space and is suitable for optimization problems with simulation-based objective functions is presented. The splitting rule is based on the representation of a convex relaxation of the search space in terms of a minimum spanning tree and adopts ideas from multilevel coordinate search. The objective function is underestimated on its domain by a convex quadratic function. The main motivation is the aim to find—for a vehicle and environment specification—a configuration of the tyres such that the energy losses caused by them are minimized. Numerical tests on a set of optimization problems are presented to compare the performance of the algorithm developed with that of other existing algorithms.

Page Manager: Webmaster|Last update: 9/11/2012

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