  This paper designs a distributed stochastic annealing algorithm for
non-convex cooperative aggregative games, whose agents' cost functions not only
depend on agents' own decision variables but also rely on the sum of agents'
decision variables. To seek the the social optimum of cooperative aggregative
games, a distributed stochastic annealing algorithm is proposed, where the
local cost functions are non-convex and the communication topology between
agents is time varying. The weak convergence to the social optimum of the
algorithm is further analyzed. A numerical example is given to illustrate the
effectiveness of the proposed algorithm.
