  In this paper we propose a modified version of the simulated annealing
algorithm for solving a stochastic global optimization problem. More precisely,
we address the problem of finding a global minimizer of a function with noisy
evaluations. We provide a rate of convergence and its optimized parametrization
to ensure a minimal number of evaluations for a given accuracy and a confidence
level close to 1. This work is completed with a set of numerical
experimentations and assesses the practical performance both on benchmark test
cases and on real world examples.
