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Adaptive modeling strategy for constrained global optimization with application to aerodynamic wing design.

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Title: Adaptive modeling strategy for constrained global optimization with application to aerodynamic wing design.
Authors: Bartoli, N.1 (AUTHOR) nathalie.bartoli@onera.fr, Lefebvre, T.1 (AUTHOR), Dubreuil, S.1 (AUTHOR), Olivanti, R.2 (AUTHOR), Priem, R.1 (AUTHOR), Bons, N.3 (AUTHOR), Martins, J.R.R.A.3 (AUTHOR), Morlier, J.4 (AUTHOR)
Source: Aerospace Science & Technology. Jul2019, Vol. 90, p85-102. 18p.
Subjects: Constrained optimization, Global optimization, Mathematical optimization, Computational fluid dynamics, Structural optimization, Surrogate-based optimization
Abstract: Surrogate models are often used to reduce the cost of design optimization problems that involve computationally costly models, such as computational fluid dynamics simulations. However, the number of evaluations required by surrogate models usually scales poorly with the number of design variables, and there is a need for both better constraint formulations and multimodal function handling. To address this issue, we developed a surrogate-based gradient-free optimization algorithm that can handle cases where the function evaluations are expensive, the computational budget is limited, the functions are multimodal, and the optimization problem includes nonlinear equality or inequality constraints. The proposed algorithm—super efficient global optimization coupled with mixture of experts (SEGOMOE)—can tackle complex constrained design optimization problems through the use of an enrichment strategy based on a mixture of experts coupled with adaptive surrogate models. The performance of this approach was evaluated for analytic constrained and unconstrained problems, as well as for a multimodal aerodynamic shape optimization problem with 17 design variables and an equality constraint. Our results showed that the method is efficient and that the optimum is much less dependent on the starting point than the conventional gradient-based optimization. [ABSTRACT FROM AUTHOR]
Copyright of Aerospace Science & Technology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 136729758
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Adaptive modeling strategy for constrained global optimization with application to aerodynamic wing design.
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  Data: <searchLink fieldCode="JN" term="%22Aerospace+Science+%26+Technology%22">Aerospace Science & Technology</searchLink>. Jul2019, Vol. 90, p85-102. 18p.
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  Data: Surrogate models are often used to reduce the cost of design optimization problems that involve computationally costly models, such as computational fluid dynamics simulations. However, the number of evaluations required by surrogate models usually scales poorly with the number of design variables, and there is a need for both better constraint formulations and multimodal function handling. To address this issue, we developed a surrogate-based gradient-free optimization algorithm that can handle cases where the function evaluations are expensive, the computational budget is limited, the functions are multimodal, and the optimization problem includes nonlinear equality or inequality constraints. The proposed algorithm—super efficient global optimization coupled with mixture of experts (SEGOMOE)—can tackle complex constrained design optimization problems through the use of an enrichment strategy based on a mixture of experts coupled with adaptive surrogate models. The performance of this approach was evaluated for analytic constrained and unconstrained problems, as well as for a multimodal aerodynamic shape optimization problem with 17 design variables and an equality constraint. Our results showed that the method is efficient and that the optimum is much less dependent on the starting point than the conventional gradient-based optimization. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Aerospace Science & Technology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.ast.2019.03.041
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        Text: English
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              Text: Jul2019
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