A splitting algorithm for simulation-based optimization problems with categorical variables
| Autor: | Christoffer Cromvik, Zuzana Nedělková, Peter Lindroth, Michael Patriksson, Ann-Brith Strömberg |
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| Přispěvatelé: | Publica |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
021103 operations research
Control and Optimization Computer science Applied Mathematics 0211 other engineering and technologies 02 engineering and technology Management Science and Operations Research Industrial and Manufacturing Engineering Computer Science Applications Simulation-based optimization Product (mathematics) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Point (geometry) Finite set Categorical variable Algorithm Computer Science::Databases |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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