Expected Hypervolume Maximization for Multiobjective Optimization under Uncertainties

This paper addresses the problem of multiobjective optimization under uncertainties by reformulating it as a Bayesian decision problem. Instead of simply taking the expectation of each objective, the authors propose to maximize the expected value of the hypervolume indicator. The approach uses gradient-based methods within a stochastic optimization framework, with careful handling of dominated points.