Multicast routing with multiple constraints in high-speed networks based on genetic algorithms
ICCC '02 Proceedings of the 15th international conference on Computer communication
Multicast routing algorithm based on extended simulated annealing algorithm
MMACTE'05 Proceedings of the 7th WSEAS International Conference on Mathematical Methods and Computational Techniques In Electrical Engineering
Solving the Delay Constrained Multicast Routing Problem Using the Transiently Chaotic Neural Network
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
A GA-based QoS multicast routing algorithm for large-scale networks
International Journal of High Performance Computing and Networking
A neural network based application layer multicast routing protocol
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Framework of a flexible computer communication network
Computer Communications
QoS routing based on genetic algorithm
Computer Communications
GA-based heuristic algorithms for bandwidth-delay-constrained least-cost multicast routing
Computer Communications
Bandwidth-delay-constrained least-cost multicast routing based on heuristic genetic algorithm
Computer Communications
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Real-time communication networks are designed mainly to support multimedia applications, especially the interactive ones, which require a guarantee of Quality of Service (QoS). Moreover, multicasting is needed as there are usually more than two peers who communicate together using multimedia applications. As for the routing, the network has to find an optimum (least cost) multicast route, that has enough resources to provide or guarantee the required QoS. This problem is called QoS constrained multicast routing and was proved to be an NP-complete problem. In contrast to the existing heuristic approaches, in this paper we propose a modified version of a Hopfield neural network model to solve QoS (delay) constrained multicast routing. By the massive parallel computation of neural networks, it can find a near optimal multicast route very fast, when implemented in hardware. Simulation results show that the proposed model has performance near to the optimal solution and comparable to existing heuristics