Towards an efficient global multidisciplinary design optimization algorithm

• Published in: Structural and multidisciplinary optimization Vol. 62; no. 4; pp. 1739 - 1765
• Main Authors: Dubreuil, S., Bartoli, N., Gogu, C., Lefebvre, T.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2020; Springer Verlag
• Subjects: Computational Mathematics and Numerical Analysis; Engineering; Engineering Design; Mathematics; Optimization and Control; Research Paper; Theoretical and Applied Mechanics; Gaussian Process; Global optimization; Multidisciplinary design optimization; Multidisciplinary Design Optimization
• ISSN: 1615-147X; 1615-1488; 1615-1488
• DOI: 10.1007/s00158-020-02514-6

This article proposes a new surrogate-based multidisciplinary design optimization algorithm. The main idea is to replace each disciplinary solver involved in a non-linear multidisciplinary analysis by Gaussian process surrogate models. Although very natural, this approach creates difficulties as the non-linearity of the multidisciplinary analysis leads to a non-Gaussian model of the objective function. However, in order to follow the path of classical Bayesian optimization such as the efficient global optimization algorithm, a dedicated model of the non-Gaussian random objective function is proposed. Then, an Expected Improvement criterion is proposed to enrich the disciplinary Gaussian processes in an iterative procedure that we call efficient global multidisciplinary design optimization (EGMDO). Such an adaptive approach allows to focus the computational budget on areas of the design space relevant only with respect to the optimization problem. The obtained reduction of the number of solvers evaluations is illustrated on a classical MDO test case and on an engineering test case.