Nonconvex maximization for communication systems based on particle swarm optimization

We consider the network utility maximization problem in networks. Since the objective function with the inelastic traffic is nonconcave, it is difficult to solve this nonconvex optimization problem. This paper presents an algorithm using particle swarm optimization (PSO) where the objective is to maximize the aggregate source utility over the transmission rate. PSO is a new evolution algorithm based on the movement and intelligence of swarms looking for the most fertile feeding location, which can solve discontinuous, nonconvex and nonlinear problems efficiently. It is proved that the proposed algorithm converges to the optimal solutions in this paper. Numerical examples show that our algorithm can guarantee the fast convergence only by a few iterations. It also demonstrates that our algorithm can efficiently solve the nonconvex optimization problems when we study the different utility functions in more realistic settings.