Research On QoS Multicast Routing Algorithm Based On Swarm Intelligence Optimization Algorithm

With the rapid development of computer network technology, more and morecommunication demands appear, especially the rise of distance learning, video conference,IPTV, network game and so on. These applications have proposed higher and more urgentrequirements on the bearing capacity of existing network, at the same time, the computer isrequired to use a better way of communication namely the multicast communication when itsupports multimedia business. Multimedia real-time business has different requirements onQoS parameters, such as time delay, bandwidth, cost, packet loss rate. Multi-constraints QoSmulticast routing algorithm has became one of hot issues in the field of computer networkresearch.On the basis of multi-constraints QoS multicast routing algorithm, the paper built themathematical model of QoS multicast routing, and proposed a new algorithm named theCombination of Genetic Algorithm and Ant Colony Algorithm (CGAACA), which combinedgenetic algorithm and ant colony algorithm effectively. On the early stage of algorithm, a fewgroups of optimization solutions were generated; on the middle stage, in order to ensure thefusion of genetic algorithm and ant colony algorithm at the best time, the genetic algorithmevolution degree function was given to control the best fusion time of the two algorithms; onthe late stage, some optimization solutions of genetic algorithm were converted intopheromone initial value of ant colony algorithm, and then used ant colony algorithm to get theoptimal solution which meet QoS constraints. In addition, the paper added the concept ofneighborhood search on the early and late stage of algorithm, and used the maximumdifference crossover strategy, optimal choice strategy and dual pheromone update strategy.Because of these new strategies, the algorithm not only overcame shortcomings of the slowevolution of genetic algorithm’s late stage and the lack pheromone of ant colony algorithm’searly stage, but also remained advantages of parallelism of genetic algorithm and positivefeedback of ant colony algorithm. The paper applied the Combination of Genetic Algorithm and Ant Colony Algorithm toQoS multicast routing, and used Matlab to simulate. The results have showed that thisalgorithm not only has feasibility and effectiveness but also has better global convergencecomparing with the genetic algorithm and ant colony algorithm. At the same time, it achieveseffective optimization of network resources and offers a certain theoretical support on thefuture development of network.