  Selection of perefect parameters for low-pass filters can sometimes be an
expensive problem with no analytical solution or differentiability of cost
function. In this paper, we introduce a new PSO-inspired algorithm, that
incorporates the positive experiences of the swarm to learn the geometry of the
search space,thus obtaining the ability to consistently reach global optimum
and is especially suitable for nonsmooth semiconvex functions optimization. We
compare it to a set of other algorithms on test functions of choice to prove
it's suitability to a certain range of problems, and then apply it to the
problem of finding perfect parameters for exponential smoothing algorithm.
