Fast and Accurate Bayesian Optimization with Pre-trained Transformers for Constrained Engineering Problems
CoRR(2024)
摘要
Bayesian Optimization (BO) is a foundational strategy in the field of
engineering design optimization for efficiently handling black-box functions
with many constraints and expensive evaluations. This paper introduces a fast
and accurate BO framework that leverages Pre-trained Transformers for Bayesian
Optimization (PFN4sBO) to address constrained optimization problems in
engineering. Unlike traditional BO methods that rely heavily on Gaussian
Processes (GPs), our approach utilizes Prior-data Fitted Networks (PFNs), a
type of pre-trained transformer, to infer constraints and optimal solutions
without requiring any iterative retraining. We demonstrate the effectiveness of
PFN-based BO through a comprehensive benchmark consisting of fifteen test
problems, encompassing synthetic, structural, and engineering design
challenges. Our findings reveal that PFN-based BO significantly outperforms
Constrained Expected Improvement and Penalty-based GP methods by an order of
magnitude in speed while also outperforming them in accuracy in identifying
feasible, optimal solutions. This work showcases the potential of integrating
machine learning with optimization techniques in solving complex engineering
challenges, heralding a significant leap forward for optimization
methodologies, opening up the path to using PFN-based BO to solve other
challenging problems, such as enabling user-guided interactive BO, adaptive
experiment design, or multi-objective design optimization. Additionally, we
establish a benchmark for evaluating BO algorithms in engineering design,
offering a robust platform for future research and development in the field.
This benchmark framework for evaluating new BO algorithms in engineering design
will be published at https://github.com/rosenyu304/BOEngineeringBenchmark.
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