This article focuses on the multi-objective optimization of stochastic simulators with high output variance, where the input space is finite and the objective functions are expensive to evaluate. We rely on Bayesian optimization algorithms, which use probabilistic models to make predictions about the functions to be optimized. The proposed approach is an extension of the Pareto Active Learning (PAL) algorithm for the estimation of Pareto-optimal solutions that makes it suitable for the stochastic setting. We named it Pareto Active Learning for Stochastic Simulators (PALS). The performance of PALS is assessed through numerical experiments over a set of bi-dimensional, bi-objective test problems. PALS exhibits superior performance when compared to other scalarization-based and random-search approaches.
翻译:本条的重点是对产出差异大的随机模拟器进行多客观优化,输入空间是有限的,客观功能是昂贵的评估。我们依靠巴伊西亚优化算法,这种算法使用概率模型对要优化的功能作出预测。提议的方法是Pareto主动学习算法的延伸,用于估计适合随机环境的Pareto最佳解决方案。我们称之为Pareto积极学习用于存储模拟器(PALS)。PALS的性能是通过一组双维、双目标测试问题的数字实验来评估的。PALS与其他基于缩放和随机研究的方法相比,表现优异。