In SMT, the instability of MERT, the commonly used optimizer, is an acknowledged problem. This paper presents two methods for smoothing the MERT instability. Both exploit a set of different realizations of the same system obtained by running the optimization stage multiple times. One method averages the sets of different optimal weights; the other combines the translations generated by the various realizations. Experiments conducted on two different sized tasks involving four different language pairs show that both methods are effective in smoothing instability, but also that the average system well competes with the more expensive system combination.