Towards an efficient global multidisciplinary design optimization algorithm
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...
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| Published in | Structural and multidisciplinary optimization Vol. 62; no. 4; pp. 1739 - 1765 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.10.2020
Springer Verlag |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1615-147X 1615-1488 1615-1488 |
| DOI | 10.1007/s00158-020-02514-6 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Bartoli, N. Gogu, C. Lefebvre, T. Dubreuil, S. |
| Author_xml | – sequence: 1 givenname: S. surname: Dubreuil fullname: Dubreuil, S. email: sylvain.dubreuil@onera.fr organization: ONERA/DTIS, Université de Toulouse – sequence: 2 givenname: N. orcidid: 0000-0002-6451-2203 surname: Bartoli fullname: Bartoli, N. organization: ONERA/DTIS, Université de Toulouse – sequence: 3 givenname: C. orcidid: 0000-0002-7278-5631 surname: Gogu fullname: Gogu, C. organization: Institut Clément Ader (ICA), CNRS, UPS, INSA, ISAE, Mines Albi, Université de Toulouse – sequence: 4 givenname: T. surname: Lefebvre fullname: Lefebvre, T. organization: ONERA/DTIS, Université de Toulouse |
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| Cites_doi | 10.3166/remn.15.81-92 10.1023/A:1008306431147 10.1007/BF01197554 10.1007/s00158-015-1379-9 10.1023/B:OPTE.0000048536.47956.62 10.2514/1.45790 10.2514/1.J051895 10.1007/978-1-4612-3094-6 10.1115/1.4005619 10.1007/s10208-004-0119-0 10.1115/1.4038333 10.1007/s00158-018-2032-1 10.1137/0804044 10.1115/1.4034110 10.1017/S0962492900002841 10.1115/1.4031096 10.1016/j.cma.2019.04.013 10.1007/s00158-017-1733-1 10.1007/s00158-012-0763-y 10.1002/nme.4364 10.1016/j.cma.2018.01.009 10.1007/s00158-013-0919-4 10.1016/j.ast.2019.03.041 10.1007/s00158-016-1488-0 10.1007/s00158-007-0121-7 10.2514/6.1996-714 10.1109/DCABES.2017.48 10.2514/6.2018-3745 |
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| Issue | 4 |
| Keywords | Gaussian Process Global optimization Multidisciplinary design optimization Multidisciplinary Design Optimization |
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| Snippet | This article proposes a new surrogate-based multidisciplinary design optimization algorithm. The main idea is to replace each disciplinary solver involved in a... This article proposes a new surrogate based Multidisciplinary Design Optimization algorithm. The main idea is to replace each disciplinary solver involved in a... |
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| SubjectTerms | Computational Mathematics and Numerical Analysis Engineering Engineering Design Mathematics Optimization and Control Research Paper Theoretical and Applied Mechanics |
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| Title | Towards an efficient global multidisciplinary design optimization algorithm |
| URI | https://link.springer.com/article/10.1007/s00158-020-02514-6 https://hal.science/hal-02904829 |
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