The fast-growing need for grey-box and black-box optimization methods for constrained global optimization problems in fields such as medicine, chemistry, engineering and artificial intelligence, has led to the development of new efficient algorithms for finding the best possible solution. In this work, we present DEFT-FUNNEL, an open-source global optimization algorithm for general constrained grey-box and black-box problems that belongs to the class of trust-region sequential quadratic optimization algorithms. Polynomial interpolation models are used as surrogates for the black-box functions and a clustering-based multistart strategy is applied for searching for the global minima. Numerical experiments show that DEFT-FUNNEL compares favorably with state-of-the-art methods on two sets of benchmark problems: one set containing problems where every function is a black box and another set with problems where some of the functions and their derivatives are known to the solver. The code as well as the test sets used for experiments are available at the Github repository http://github.com/phrsampaio/deft-funnel.

在医学、化学、工程和人工智能等领域，对用于约束全局优化问题的灰盒和黑盒优化方法的快速增长的需求导致了新的高效算法的开发，以寻找可能的最佳解决方案。在这项工作中，我们提出了 DEFT-FUNNEL，一种用于一般约束灰盒和黑盒问题的开源全局优化算法，属于信任区域序列二次优化算法类。多项式插值模型用作黑盒函数的替代品，并应用基于聚类的多起始策略来搜索全局最小值。数值实验表明，DEFT-FUNNEL 在两组基准问题上优于最先进的方法：一组包含每个函数都是黑盒的问题，另一组包含求解器知道某些函数及其导数的问题。用于实验的代码和测试集可在 Github 存储库 http://github.com/phrsampaio/deft-funnel 中获得。