  We address an optimization problem where the cost function is the expectation
of a random mapping. To tackle the problem two approaches based on the
approximation of the objective function by consensus-based particle
optimization methods on the search space are developed. The resulting methods
are mathematically analyzed using a mean-field approximation and their
connection is established. Several numerical experiments show the validity of
the proposed algorithms and investigate their rates of convergence.
