论文标题
使用回归和分类的二进制约束对帕累托前沿的自适应采样
Adaptive Sampling of Pareto Frontiers with Binary Constraints Using Regression and Classification
论文作者
论文摘要
我们提出了一种新型的自适应优化算法，用于黑盒多目标优化问题，并在贝叶斯优化的基础上具有二进制约束。我们的方法基于概率回归和分类模型，这些模型是优化目标的替代品，并允许我们在每次迭代中立即提出多个设计点。拟议的采集功能是可以直观地理解的，并且可以根据手头问题的要求进行调整。我们还提出了一种新颖的椭圆形截断方法，以直接的方式以正常概率密度的回归模型以直接的方式加快预期的超量计算。我们通过进化算法在多个测试问题上基准我们的方法。
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.
