  In derivative-free and blackbox optimization, the objective function is often
evaluated through the execution of a computer program seen as a blackbox. It
can be noisy, in the sense that its outputs are contaminated by random errors.
Sometimes, the source of these errors is identified and controllable, in the
sense that it is possible to reduce the standard deviation of the stochastic
noise it generates. A common strategy to deal with such a situation is to
monotonically diminish this standard deviation, to asymptotically make it
converge to zero and ensure convergence of algorithms because the noise is
dismantled. This work presents MpMads, an algorithm which follows this
approach. However, in practice a reduction of the standard deviation increases
the computation time, and makes the optimization process long. Therefore, a
second algorithm called DpMads is introduced to explore another strategy, which
does not force the standard deviation to monotonically diminish. Although these
strategies are proved to be theoretically equivalents, tests on analytical
problems and an industrial blackbox are presented to illustrate practical
differences.
