Black-box optimization (BBO) has a broad range of applications, including automatic machine learning, experimental design, and database knob tuning. However, users still face challenges when applying BBO methods to their problems at hand with existing software packages in terms of applicability, performance, and efficiency. This paper presents OpenBox, an open-source BBO toolkit with improved usability. It implements user-friendly inferfaces and visualization for users to define and manage their tasks. The modular design behind OpenBox facilitates its flexible deployment in existing systems. Experimental results demonstrate the effectiveness and efficiency of OpenBox over existing systems. The source code of OpenBox is available at https://github.com/PKU-DAIR/open-box.

翻译：黑盒优化（BBO）具有广泛的应用，包括自动机器学习、实验设计和数据库参数调优。然而，在使用现有软件包将BBO方法应用于具体问题时，用户仍面临适用性、性能和效率方面的挑战。本文提出了OpenBox，一个具有更高易用性的开源BBO工具包。它实现了用户友好的接口和可视化功能，便于用户定义和管理其任务。OpenBox背后的模块化设计使其能够灵活部署在现有系统中。实验结果表明，OpenBox在有效性和效率上均优于现有系统。OpenBox的源代码可在https://github.com/PKU-DAIR/open-box获取。