  In this paper, we apply the stochastic search dynamic optimization framework
for steering opinion dynamics in a partially active population. The framework
is grounded on stochastic optimization theory and relies on sampling of
candidate solutions from distributions of the exponential family. We derive the
distribution parameter update for an open loop, a feedback, and a novel
adaptive feedback policy. All three policies are tested on two different
opinion dynamics steering scenarios in simulation and compared against a hand
designed feedback policy. The results showcase the effectiveness the framework
for opinion dynamics control and the robustness of the adaptive feedback policy
with respect to the active agent set size.
