Network Congestion Control Method Based On Immune Particle Swarm Optimization

With the recent promotion of computer applications, people’s needs and the requirements of the network scale is becoming higher and higher, the rapid development of network led to the network congestion phenomenon occurred. Network size continues to expand, the rapid expansion of network traffic, often resulting in network congestion phenomenon. In order to ensure the normal operation of the network, we need a higher network quality of service (QoS) to meet the needs of network development. There is network congestion restricts the development and application of the network, to take reasonable measures to prevent and control network congestion has important significance. With traditional optimization methods to control network congestion though constantly improving, but there is always this or that problem. In recent years, against the shortcomings of traditional optimization algorithms, domestic and foreign scholars intelligent optimization algorithm is applied to the network congestion control them, to achieve the network congestion control has become a hot topic in the current through the intelligent optimization algorithm.Intelligent optimization algorithms is a new optimization algorithm by unconscious behavior optimization to adapt to the living environment and living conditions of the optimization. So far there have been a variety of intelligent optimization algorithms. In this thesis, artificial immune algorithm and particle swarm optimization are combined network congestion control which is applied to the specific contents are as follows:(1) Firstly, analyzes the phenomenon of network congestion, reasons for the formation of network congestion,the network congestion control mechanisms and the network congestion control algorithm, also analyzes the application of NS2network simulation software.(2) Establish the network topology model of network congestion control, and the network topology and network routing model are analyzed and discussed. Based on the analysis of the network path optimization proposed the problem of the route optimization about network congestion. (3) The immune particle swarm optimization algorithm is proposed it combines the artificial immune and particle swarm optimization algorithm. The immune particle swarm optimization put the immune system of the information processing mechanism into particle swarm optimization algorithm, based on PSO algorithm fast convergence characteristics, using artificial immune algorithm features diversity to avoid particle swarm optimization falling into local solution, improved PSO late convergence speed of the algorithm.(4)Gives the network congestion control algorithm based on immune particle swarm optimization, viewed the consumption of resources distribution and load balancing as a network path optimization goals, after meeting the bandwidth, delay, expense and a series premise, consumes less network resources as much as possible while the load distribution on the link have ample resources and effective implementation of network congestion control. Simulation results show that immune particle swarm optimization algorithm is effective and reliable.