  The objective of this Philosophiae Doctor (Ph.D) thesis is to propose an
efficient approach for optimizing a multidisciplinary black-box model when the
optimization problem is constrained and involves a large number of mixed
integer design variables (typically 100 variables). The targeted optimization
approach, called EGO, is based on a sequential enrichment of an adaptive
surrogate model and, in this context, GP surrogate models are one of the most
widely used in engineering problems to approximate time-consuming high fidelity
models. EGO is a heuristic BO method that performs well in terms of solution
quality. However, like any other global optimization method, EGO suffers from
the curse of dimensionality, meaning that its performance is satisfactory on
lower dimensional problems, but deteriorates as the dimensionality of the
optimization search space increases. For realistic aircraft design problems,
the typical size of the design variables can even exceed 100 and, thus, trying
to solve directly the problems using EGO is ruled out. The latter is especially
true when the problems involve both continuous and categorical variables
increasing even more the size of the search space. In this Ph.D thesis,
effective parameterization tools are investigated, including techniques like
partial least squares regression, to significantly reduce the number of design
variables. Additionally, Bayesian optimization is adapted to handle discrete
variables and high-dimensional spaces in order to reduce the number of
evaluations when optimizing innovative aircraft concepts such as the "DRAGON"
hybrid airplane to reduce their climate impact.
