Multi-objective Bayesian Optimization With Mixed-categorical Design Variables for Expensive-to-evaluate Aeronautical Applications

TL;DR

This paper introduces SEGOMOE, a Bayesian optimization method tailored for complex, expensive aeronautical system design problems involving mixed variables and multiple objectives, demonstrating effectiveness through practical aerospace applications.

Contribution

The paper presents a novel surrogate-based Bayesian optimization approach capable of handling mixed variables and multi-objective problems in aeronautical engineering, with an in-house implementation called SEGOMOE.

Findings

SEGOMOE effectively constructs Pareto fronts with minimal evaluations.

The method handles high-dimensional, mixed-variable problems in aerospace design.

Demonstrated success on three realistic aeronautical case studies.

Abstract

This work aims at developing new methodologies to optimize computational costly complex systems (e.g., aeronautical engineering systems). The proposed surrogate-based method (often called Bayesian optimization) uses adaptive sampling to promote a trade-off between exploration and exploitation. Our in-house implementation, called SEGOMOE, handles a high number of design variables (continuous, discrete or categorical) and nonlinearities by combining mixtures of experts for the objective and/or the constraints. Additionally, the method handles multi-objective optimization settings, as it allows the construction of accurate Pareto fronts with a minimal number of function evaluations. Different infill criteria have been implemented to handle multiple objectives with or without constraints. The effectiveness of the proposed method was tested on practical aeronautical applications within the context of the European Project AGILE 4.0 and demonstrated favorable results. A first example concerns a retrofitting problem where a comparison between two optimizers have been made. A second example introduces hierarchical variables to deal with architecture system in order to design an aircraft family. The third example increases drastically the number of categorical variables as it combines aircraft design, supply chain and manufacturing process. In this article, we show, on three different realistic problems, various aspects of our optimization codes thanks to the diversity of the treated aircraft problems.