Bayesian optimization has emerged at the forefront of expensive black-box optimization due to its data efficiency. Recent years have witnessed a proliferation of studies on the development of new Bayesian optimization algorithms and their applications. Hence, this paper attempts to provide a comprehensive and updated survey of recent advances in Bayesian optimization and identify interesting open problems. We categorize the existing work on Bayesian optimization into nine main groups according to the motivations and focus of the proposed algorithms. For each category, we present the main advances with respect to the construction of surrogate models and adaptation of the acquisition functions. Finally, we discuss the open questions and suggest promising future research directions, in particular with regard to heterogeneity, privacy preservation, and fairness in distributed and federated optimization systems.

翻译：由于数据效率的提高,贝叶斯优化已成为昂贵黑盒优化的最前沿,近年来,关于开发新的巴伊斯优化算法及其应用的研究越来越多,因此,本文件试图对巴伊斯优化的最新进展进行全面和最新调查,并找出令人感兴趣的开放问题。我们根据拟议算法的动机和重点,将巴伊斯优化的现有工作分为九大类。我们为每一类介绍了在建造代用模型和调整购置功能方面的主要进展。最后,我们讨论了开放问题,并建议有希望的未来研究方向,特别是在分布式和联合式优化系统中的异质性、隐私保护以及公平性方面。