Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design

Abstract

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.

AIAA SciTech 2022 Forum. arXiv admin note: substantial text overlap with arXiv:2402.04711

References in corpus (5)

• 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
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Cited by in corpus (3)

• System Architecture Optimization Strategies: Dealing with Expensive Hierarchical Problems
• Surrogate-based optimization of system architectures subject to hidden constraints
• High-dimensional mixed-categorical Gaussian processes with application to multidisciplinary design optimization for a green aircraft