A splitting algorithm for simulation-based optimization problems with categorical variables
Journal article, 2019

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.


Design optimization

simulation-based optimization

categorical variables



Zuzana Nedelkova

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Christoffer Cromvik

Fraunhofer-Chalmers Centre

Peter Lindroth

Volvo Group

Michael Patriksson

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Ann-Brith Strömberg

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Engineering Optimization

0305-215X (ISSN) 1029-0273 (eISSN)

Vol. 51 5 815-831

TyreOpt - Fuel consumption reduction by tyre drag optimization

Swedish Energy Agency (2011-001831), 2012-01-01 -- 2015-12-31.

Chalmers, 2012-01-01 -- 2018-05-04.

Driving Forces

Sustainable development

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Materials Science

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Computational Mathematics


Basic sciences



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