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: https://github.com/an-on-ym-ous/lbn_mobo

翻译：贝叶斯优化为全局优化黑箱、评估代价高昂的函数提供了强有力的框架。然而，由于默认高斯过程代理的可扩展性较差，其在处理数据密集型问题（尤其是在多目标场景下）的能力有限。我们提出了一种新颖的贝叶斯优化框架，专门用于解决这些局限性。我们的方法采用贝叶斯神经网络方法进行代理建模。这能够高效处理大批量数据、建模复杂问题，并生成预测的不确定性。此外，我们的方法结合了一种基于著名且易于部署的NSGA-II的可扩展、具备不确定性感知的采集策略。这种完全可并行的策略促进了未知区域的高效探索。我们的框架允许在数据密集型环境中以最少的迭代次数实现有效优化。通过与最先进的多目标优化方法进行比较，我们证明了本方法的优越性。我们在两个实际问题——翼型设计和彩色打印——上进行了评估，展示了我们方法的适用性和效率。代码见：https://github.com/an-on-ym-ous/lbn_mobo