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High-dimensional efficient global optimization using both random and supervised embeddings

Priem, Remy and Diouane, Youssef and Bartoli, Nathalie and Dubreuil, Sylvain and Saves, Paul High-dimensional efficient global optimization using both random and supervised embeddings. (2023) In: AIAA AVIATION 2023 FORUM, 12 June 2023 - 16 June 2023 (San Diego, United States).

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Official URL: https://doi.org/10.2514/6.2023-4448


Bayesian optimization (BO) is one of the most powerful strategies to solve expensive black-box optimization problems. However, BO methods are conventionally used for optimization problems of small dimension because of the curse of dimensionality. In this paper, to solve high dimensional optimization problems, we propose to incorporate linear embedding subspaces of small dimension to efficiently perform the optimization. An adaptive learning strategy for these linear embeddings is carried out in conjunction with the optimization. The resulting BO method, named EGORSE, combines in an adaptive way both random and supervised linear embeddings. EGORSE has been compared to state-of-the-art algorithms and tested on academic examples with a number of design variables ranging from 10 to 600. The obtained results show the high potential of EGORSE to solve high-dimensional black-box optimization problems, both in terms of CPU time and number of calls to the expensive black-box.

Item Type:Conference or Workshop Item (Paper)
HAL Id:hal-04123603
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:Other partners > Direction Générale pour l'Armement - DGA (FRANCE)
Other partners > Ecole Polytechnique de Montréal (CANADA)
Université de Toulouse > Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE)
French research institutions > Office National d'Etudes et Recherches Aérospatiales - ONERA (FRANCE)
Laboratory name:
Deposited On:09 Jun 2023 10:12

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