Lagrange multipliers and optimality
SIAM Review
Fair end-to-end window-based congestion control
IEEE/ACM Transactions on Networking (TON)
Utility-based rate control in the Internet for elastic traffic
IEEE/ACM Transactions on Networking (TON)
A duality model of TCP and queue management algorithms
IEEE/ACM Transactions on Networking (TON)
End-to-end congestion control schemes: utility functions, random losses and ECN marks
IEEE/ACM Transactions on Networking (TON)
Mathematics and Computers in Simulation
Optimal multi-thresholding using a hybrid optimization approach
Pattern Recognition Letters
Downlink power allocation for multi-class wireless systems
IEEE/ACM Transactions on Networking (TON)
Network utility maximization for triple-play services
Computer Communications
Motion planning in order to optimize the length and clearance applying a Hopfield neural network
Expert Systems with Applications: An International Journal
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fundamental design issues for the future Internet
IEEE Journal on Selected Areas in Communications
Real time cross layer design using particle swarm optimization
COMPUTE '11 Proceedings of the Fourth Annual ACM Bangalore Conference
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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.