NomadBBO：一个用于Python中约束混合变量黑箱优化的直接搜索框架
NomadBBO: A direct-search framework for constrained mixed-variable blackbox optimization in Python
- GERAD and Department of Mathematics and Industrial Engineering, Polytechnique Montréal（GERAD 与蒙特利尔理工学院数学与工业工程系）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
NomadBBO是一个Python库，基于CatADS直接搜索方法，扩展自适应直接搜索至混合变量问题，通过渐进障碍处理约束，集成Nomad后端，在Cat-Suite基准上验证了性能。
AI中文摘要：
混合变量黑箱优化出现在基于模拟的工程和机器学习中，其中目标和约束函数的评估成本高昂，变量可能是连续的、整数的、二进制的或分类的。已有多个软件库可用于无导数优化，但很少有库能同时具备易用性、计算效率、灵活性和理论收敛保证。本工作介绍了NomadBBO，一个围绕Nomad构建的用户友好的Python库，用于不等式约束的混合变量黑箱优化。其核心优化框架是CatADS，一种新的直接搜索方法，将自适应直接搜索（ADS）扩展到混合变量问题。CatADS结合了ADS用于定量变量的机制与用于处理分类变量的邻域。这些邻域使用基于高斯过程或经验Wasserstein距离，这些距离从可用数据中推导得出。不等式约束通过渐进障碍处理。混合变量和基于代理的机制在Python中实现，并通过Cython连接到Nomad高效的C++后端。这种集成使Nomad能够处理分类变量，还允许使用外部库和混合优化策略，包括来自SMT 2.0的高斯过程模型和贝叶斯优化。数值实验将NomadBBO与Cat-Suite基准集合中的约束和无约束混合变量问题上的其他求解器进行了比较。测试版可在https URL获取。
英文摘要：
Mixed-variable blackbox optimization arises in simulation-based engineering and machine learning, where objective and constraint functions are expensive to evaluate and variables may be continuous, integer, binary, or categorical. Several software libraries are available for derivative-free optimization, but few combine ease of use, computational efficiency, flexibility, and theoretical convergence guarantees. This work presents NomadBBO, a user-friendly Python library for inequality-constrained mixed-variable blackbox optimization built around Nomad. Its core optimization framework is CatADS, a new direct-search method that extends Adaptive Direct Search (ADS) to mixed-variable problems. CatADS combines the mechanisms of ADS for quantitative variables with neighborhoods for handling categorical variables. These neighborhoods use Gaussian process-based or empirical Wasserstein distances derived from available data. Inequality constraints are handled through the progressive barrier. The mixed-variable and surrogate-based mechanisms are implemented in Python and connected to the efficient C++ backend of Nomad via Cython. This integration allows Nomad to handle categorical variables. It also enables the use of external libraries and hybrid optimization strategies, including Gaussian process models from SMT 2.0 and Bayesian optimization. Numerical experiments compare NomadBBO with other solvers on constrained and unconstrained mixed-variable problems from the Cat-Suite benchmark collection. The beta release is available at https://test.pypi.org/project/NomadBBO/
