面向约束工程问题的基于预训练Transformer的快速准确贝叶斯优化
Fast and Accurate Bayesian Optimization with Pre-trained Transformers for Constrained Engineering Problems
AI总结：
本文提出基于预训练Transformer（PFN）的贝叶斯优化框架PFN4sBO，无需迭代重训练即可求解约束工程优化问题，在十五个测试问题上比传统GP方法速度提升一个数量级且精度更优，并发布工程BO评估基准。
摘要：
贝叶斯优化（BO）是工程设计优化领域的基础策略，用于高效处理具有许多约束和昂贵评估的黑盒函数。本文介绍了一种快速且准确的BO框架，该框架利用面向贝叶斯优化的预训练Transformer（PFN4sBO）来解决工程中的约束优化问题。与严重依赖高斯过程（GP）的传统BO方法不同，我们的方法利用先验数据拟合网络（PFN）——一种预训练Transformer——来推断约束和最优解，而无需任何迭代重训练。我们通过一个由十五个测试问题组成的综合基准展示了基于PFN的BO的有效性，这些问题涵盖合成、结构和工程设计挑战。我们的研究结果表明，基于PFN的BO在速度上比约束期望改进和基于惩罚的GP方法高出一个数量级，同时在识别可行最优解的准确性方面也优于它们。这项工作展示了将机器学习与优化技术相结合以解决复杂工程挑战的潜力，预示着优化方法学的重大飞跃，为使用基于PFN的BO解决其他具有挑战性的问题开辟了道路，例如实现用户引导的交互式BO、自适应实验设计或多目标设计优化。此外，我们建立了一个用于评估工程设计中BO算法的基准，为该领域的未来研究和开发提供了一个稳健的平台。该用于评估工程设计中新BO算法的基准框架将在https://github.com/rosenyu304/BOEngineeringBenchmark发布。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
