A scalable problem to benchmark robust multidisciplinary design optimization techniques

A scalable problem to benchmark robust multidisciplinary design optimization (RMDO) algorithms is proposed. This allows the user to choose the number of disciplines, the dimensions of the coupling and design variables and the extent of the feasible domain. After a description of the mathematical bac...

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Bibliographic Details
Published inOptimization and engineering Vol. 25; no. 2; pp. 941 - 958
Main Authors Aziz-Alaoui, Amine, Roustant, Olivier, De Lozzo, Matthias
Format Journal Article
LanguageEnglish
Published New York Springer US 01.06.2024
Springer Nature B.V
Subjects
Algorithms
Benchmarks
Control
Coupling
Engineering
Environmental Management
Financial Engineering
Mathematics
Mathematics and Statistics
Multidisciplinary design optimization
Operations Research/Decision Theory
Optimization
Polynomials
Quadratic programming
Random variables
Research Article
Robustness (mathematics)
Systems Theory
Uniqueness
Uncertainty quantification
Benchmarking
Robust optimization
Scalable problem
Quadratic programming
Multidisciplinary design optimization
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Summary:A scalable problem to benchmark robust multidisciplinary design optimization (RMDO) algorithms is proposed. This allows the user to choose the number of disciplines, the dimensions of the coupling and design variables and the extent of the feasible domain. After a description of the mathematical background, a deterministic version of the scalable problem is defined and the conditions on the existence and uniqueness of the solution are given. Then, this deterministic scalable problem is made uncertain by adding random variables to the coupling equations. Under classical assumptions, the existence and uniqueness of the solution of this RMDO problem is guaranteed. This solution can be easily computed with a quadratic programming algorithm and serves as a reference to assess the performance of RMDO algorithms. This scalable problem has been implemented in the open-source library GEMSEO and tested with two techniques of statistics estimation: Monte-Carlo sampling and Taylor polynomials.
ISSN:1389-4420
1573-2924
DOI:10.1007/s11081-023-09830-y