Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design
This work addresses a bottleneck in multidisciplinary design optimization for engineering systems like aircraft design, offering an incremental improvement in efficiency.
The paper tackles the issue of high hyperparameter counts in mixed-variable Bayesian optimization by constructing surrogate models with fewer hyperparameters using partial least squares and an adaptive procedure, achieving significant improvement over genetic algorithms in analytical tests and aircraft design applications.
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.