Research Of Multiple Constrained QoS Multicast Routing Optimization Algorithm

In the 21st century, computer network technology has been faster development, many of these developments comes from the emergence of new communications needs, such as distance learning, video conferencing, IPTV, online games and so on. These applications require the carrying capacity of existing networks become higher, and make computer network use a better multicast communication on supporting multimedia services. In-depth research, we found that these multimedia applications on the delay, bandwidth, cost, packet loss rate, which is QoS have different needs. Existing computer network is not able to fully satisfy the QoS targets with real-time transmission of multimedia information needs. Therefore, multiple constrained QoS multicast routing algorithm has become a hot research field of computer network problems.In this paper, we research the status of multiple constrained QoS multicast routing algorithm. QoS for the delay, bandwidth and packet loss rate and other indicators of minimum cost problems were studied, and then propose an efficient multicast routing algorithm. The main research work and results achieved are as follows:1,It examines the background knowledge of multicast routing technology, introduces the domestic and international situation, familiar with the basic concepts of QoS routing and network model, points out the advantages and disadvantages of existing algorithms, and finds a direction for this paper;2,It presents an optimization algorithm, which is based on the combination of genetic algorithm and ant colony algorithm (GAACA). The algorithm adopts genetic algorithm to produce the original results, transforms them into the initial pheromones value needed by ant colony algorithm, then uses ant colony algorithm to get the best results. This algorithm has defined a control function of genetic algorithm. It could control the appropriate combination opportunity of the two algorithms. The algorithm not only overcomes the genetic algorithm and ant colony disadvantages, while still retaining their respective advantages;3,It has improved Salama's random network topology generator algorithm. The proposed algorithm is simulated in MATLAB. And it has compared with the traditional algorithm in convergence and iterations. MATLAB run results show that the algorithm is feasible and effective. And the algorithm has the characteristics of low costs and rapid convergence.