We introduce COCO, an open-source platform for Comparing Continuous Optimizers in a black-box setting. COCO aims at automatizing the tedious and repetitive task of benchmarking numerical optimization algorithms to the greatest possible extent. The platform and the underlying methodology allow to benchmark in the same framework deterministic and stochastic solvers for both single and multiobjective optimization. We present the rationals behind the (decade-long) development of the platform as a general proposition for guidelines towards better benchmarking. We detail underlying fundamental concepts of COCO such as the definition of a problem as a function instance, the underlying idea of instances, the use of target values, and runtime defined by the number of function calls as the central performance measure. Finally, we give a quick overview of the basic code structure and the currently available test suites.

我们引入了COCO，这是一个开放源代码平台，用于在黑盒设置中比较持续优化程序。COCO旨在最大程度地自动化基准测试数值优化算法的繁琐而重复的任务。该平台和基础方法允许在同一框架中对用于单目标和多目标优化的确定性和随机求解器进行基准测试。我们提出平台（数十年）开发背后的基本原理，作为制定更好基准的指导原则。我们详细介绍了COCO的基本概念，例如，将问题定义为函数实例，实例的基本思想，目标值的使用以及由函数调用数量定义的运行时作为核心性能指标。最后，