The Research Of Network Congestion Control Based On Tabu Search Genetic Optimization

In the past 20 years, with the development of Internet, network congestion has become bottlenecks which restrict the growth and application of the network. A suitable network congestion control mechanism becomes the necessary premise for other quality of service(QoS) mechanisms to work effectively. And it is also an important means to optimize the performance and robustness of network. Therefore it is really significant to study how to control network congestion.Computer network is a highly complicated system. With the optimization theory, we can deal with network congestion effectively and achieve the purpose of congestion prevention and control. This thesis mainly focuses on the research of network congestion control method based on tabu search genetic optimization. The main contents are as follows:1. Based on the summary and analysis of network congestion control, network routing and quality of service, this essay proposes the idea which uses an optimization algorithm—tabu search genetic algorithm(TSGA) to solve the network congestion control. By applying network simulation software NS2 to network modeling and network congestion control, the simulation and research results show that it works well.2. Internet topology models are fully analyzed and studied in this paper. An optimization mathematics model of QoS routing, which provides the conditions for network routing optimization, network congestion prevention and control, is proposed on the basis of analyzing the QoS and QoS routing.3. An optimization algorithm—TSGA which combines the characteristic of tabu search(TS) and genetic algorithm(GA) is given. By the global search of GA, the individual distribute most part of region. After the solutions converge to certain extent, the TS is applied for local search. The hybrid optimization algorithm not only improves the mountain climbing of the genetic algorithm, but also provides better original solutions to tabu search, reduces the called times of tabu search, speeds up the convergence speed and gets satisfied results. Simulation results demonstrate the validity and feasibility of this hybrid optimization algorithm. 4. An optimizing on multiple constrained QoS routing, which with the objectives of network resource consumption minimization and load distribution balance, is presented in this thesis. An algorithm for network congestion control based on tabu search genetic optimization is proposed. By making simulation and performance analysis, the results manifest that this optimization algorithm can realize network congestion control, improve network performance and arrive at the purpose of congestion prevention.