This study introduces SEGOMOE, a Bayesian optimization tool for optimizing complex,
computationally expensive systems, especially in aeronautics. It efficiently handles mixed design variables
(continuous, discrete, categorical, hierarchical) using adaptive Gaussian process models. SEGOMOE
combines expert models to address nonlinearities in objectives and constraints, leveraging the
open-source Surrogate Modeling Toolbox (SMT). The tool supports multi-fidelity data and solves both
single- and multi-objective problems, including hidden constraints and high-dimensional decomposition.
Validated through benchmarks and real-world aeronautical applications, SEGOMOE proves to be robust
and versatile for tackling multidisciplinary challenges.