  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.
