We present a novel adaptive optimization algorithm for black-box multi-objective optimization problems with binary constraints on the foundation of Bayes optimization. Our method is based on probabilistic regression and classification models, which act as a surrogate for the optimization goals and allow us to suggest multiple design points at once in each iteration. The proposed acquisition function is intuitively understandable and can be tuned to the demands of the problems at hand. We also present a novel ellipsoid truncation method to speed up the expected hypervolume calculation in a straightforward way for regression models with a normal probability density. We benchmark our approach with an evolutionary algorithm on multiple test problems.

翻译：我们为黑箱多目标优化问题提出了一个新的适应性优化算法,其中含有贝斯优化基础的二进制限制。我们的方法基于概率回归和分类模型,这些模型作为优化目标的替代物,并使我们能够在每次迭代中同时提出多个设计点。提议的获取功能可以直观地理解,并且可以适应手头问题的需求。我们还提出了一个新颖的单线脱轨法,以加快预期的超大容量计算速度,为具有正常概率密度的回归模型提供直截了当的方式。我们用进化算法将我们的方法以多种测试问题作为基准。