A splitting algorithm for simulation-based optimization problems with categorical variables. Issue 5 (4th May 2019)

• Record Type: Journal Article
• Authors: Nedělková, Zuzana; Cromvik, Christoffer; Lindroth, Peter; Patriksson, Michael; Strömberg, Ann-Brith
• Is Part Of: Engineering optimization. Volume 51:Issue 5(2019)
• Page Start: 815
• Page End: 831
• Publication Date: 2019-05-04
• Subjects: Design optimization -- simulation-based optimization -- splitting -- categorical variables -- tyres; Engineering design -- Periodicals; Mathematical optimization -- Periodicals; 620.0042
• DOI: 10.1080/0305215X.2018.1495716
• Languages: English
• ISSNs: 0305-215X

Abstract

ABSTRACT: 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.