Bayesian optimization provides a powerful framework for global optimization
of black-box, expensive-to-evaluate functions. However, it has a limited
capacity in handling data-intensive problems, especially in multi-objective
settings, due to the poor scalability of default Gaussian Process surrogates.
We present a novel Bayesian optimization framework specifically tailored to
address these limitations. Our method leverages a Bayesian neural networks
approach for surrogate modeling. This enables efficient handling of large
batches of data, modeling complex problems, and generating the uncertainty of
the predictions. In addition, our method incorporates a scalable,
uncertainty-aware acquisition strategy based on the well-known, easy-to-deploy
NSGA-II. This fully parallelizable strategy promotes efficient exploration of
uncharted regions. Our framework allows for effective optimization in
data-intensive environments with a minimum number of iterations. We demonstrate
the superiority of our method by comparing it with state-of-the-art
multi-objective optimizations. We perform our evaluation on two real-world
problems - airfoil design and color printing - showcasing the applicability and
efficiency of our approach. Code is available at:
this https URL

本文提出了一种基于贝叶斯神经网络的优化框架，专门针对数据密集型环境中多目标优化的局限性进行了优化，实现了对大量数据的有效处理和复杂问题的建模，并展示了其在航空动力学和彩色印刷两个领域的应用和有效性。