High-dimensional mixed-categorical Gaussian processes with application to multidisciplinary design optimization for a green aircraft

Abstract

Recently, there has been a growing interest in mixed-categorical metamodels based on Gaussian Process (GP) for Bayesian optimization. In this context, different approaches can be used to build the mixed-categorical GP. Many of these approaches involve a high number of hyperparameters; in fact, the more general and precise the strategy used to build the GP, the greater the number of hyperparameters to estimate. This paper introduces an innovative dimension reduction algorithm that relies on partial least squares regression to reduce the number of hyperparameters used to build a mixed-variable GP. Our goal is to generalize classical dimension reduction techniques commonly used within GP (for continuous inputs) to handle mixed-categorical inputs. The good potential of the proposed method is demonstrated in both structural and multidisciplinary application contexts. The targeted applications include the analysis of a cantilever beam as well as the optimization of a green aircraft, resulting in a significant 439-kilogram reduction in fuel consumption during a single mission.

arXiv commentv2 - Structural and Multidisciplinary Optimization

References in corpus (8)

• pymoo: Multi-objective Optimization in Python
• Dealing with Integer-valued Variables in Bayesian Optimization with Gaussian Processes
• Dealing with Categorical and Integer-valued Variables in Bayesian Optimization with Gaussian Processes
• Upper Trust Bound Feasibility Criterion for Mixed Constrained Bayesian Optimization with Application to Aircraft Design
• Revisiting Bayesian Optimization in the light of the COCO benchmark
• A mixed-categorical correlation kernel for Gaussian process
• Regularized infill criteria for multi-objective Bayesian optimization with application to aircraft design
• Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design

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