Multicast Routing Optimization And Simulation Based On Artificial Bee Colony Algorithm

With the rapid development of a number of emerging multimedia realtime business application, and the Internet commercial application, high efficient QoS support becomes more and more important. QoS problem is to establish an optimal multicast tree covers all the destination nodes, which satisfying the QoS constraints. Multicast transmits the same information from the source node to multiple destination nodes in the network. The artificial bees colony algorithm(ABC) is a kind of swarm intelligent optimization algorithms, generating through simulation of the honey bees’ behavior. Compared with genetic algorithm(GA), particle swarm optimization(PSO) and other intelligence algorithms, the prominent advantages of ABC algorithm is both global and local search in each iteration, therefore, the probability of finding the optimal solution is increased, and avoid the local optimum problem in great degree.Based on the study of multicast routing algorithm and artificial bee colony algorithm, this paper generates a new multicast algorithm based on artificial bee colony algorithm. Then we improve it through combine PSO and ABC, new algorithm can automatically adjust the parameters and adapt different topologies, no longer depend on human experience.(1) For the problem that convergence time is too long in the large-scale topology for multicast algorithm, we introduce the bee colony algorithm into multicast routing algorithm optimization and put forward an artificial bee colony algorithm based multicast routing. In this algorithm, we optimize the Steiner tree directly. It is not the same as traditional methods that find paths and integrate them to generate a Steiner tree, greatly reducing the convergence time. The experiments proved this algorithm has faster convergence speed and better optimization ability in large scale than other algorithms.(2) General algorithms adjust the parameters depend on human experience and a large number of experiments for different topologies scale. This paper introduces the PSO into T-ABC, the new algorithm selects the parameters combinations adaptively through the particle searching. This makes the algorithm to get rid of the problems caused by artificial selection can not find the optimal solution, the algorithm can be applied to different topologies scale.