Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design - X-OAD -eXtended-Overall Aircraft Design Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design

Résumé

Multidisciplinary design optimization methods aim at adapting numerical optimization techniques to the design of engineering systems involving multiple disciplines. In this context, a large number of mixed continuous, integer and categorical variables might arise during the optimization process and practical applications involve a large number of design variables. Recently, there has been a growing interest in mixed variables constrained Bayesian optimization but most existing approaches severely increase the number of the hyperparameters related to the surrogate model. In this paper, we address this issue by constructing surrogate models using less hyperparameters. The reduction process is based on the partial least squares method. An adaptive procedure for choosing the number of hyperparameters is proposed. The performance of the proposed approach is confirmed on analytical tests as well as two real applications related to aircraft design. A significant improvement is obtained compared to genetic algorithms.
Fichier principal
Vignette du fichier
Scitech_paul(6).pdf (3.88 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03514915 , version 1 (06-01-2022)

Identifiants

Citer

Paul Saves, Eric Nguyen Van, Nathalie Bartoli, Thierry Lefebvre, Christophe David, et al.. Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design. AIAA SciTech 2022, Jan 2022, San Diego, United States. ⟨10.2514/6.2022-0082⟩. ⟨hal-03514915⟩
520 Consultations
405 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More