EGORSE couples random and supervised linear embeddings inside Bayesian optimization, with a constraint-based feasible-domain formulation, and shows faster CPU convergence than several high-dimensional BO baselines on benchmark problems up to 600 variables.
Design of a commercial aircraft environment control system using Bayesian optimization techniques
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abstract
In this paper, we present the application of a recently developed algorithm for Bayesian multi-objective optimization to the design of a commercial aircraft environment control system (ECS). In our model, the ECS is composed of two cross-flow heat exchangers, a centrifugal compressor and a radial turbine, the geometries of which are simultaneously optimized to achieve minimal weight and entropy generation of the system. While both objectives impact the overall performance of the aircraft, they are shown to be antagonistic and a set of trade-off design solutions is identified. The algorithm used for optimizing the system implements a Bayesian approach to the multi-objective optimization problem in the presence of non-linear constraints and the emphasis is on conducting the optimization using a limited number of system simulations. Noteworthy features of this particular application include a non-hypercubic design domain and the presence of hidden constraints due to simulation failures.
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High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings
EGORSE couples random and supervised linear embeddings inside Bayesian optimization, with a constraint-based feasible-domain formulation, and shows faster CPU convergence than several high-dimensional BO baselines on benchmark problems up to 600 variables.