An immune algorithm for the optimization of aggregated multicast

Large-scale deployment of multicast applications is limited by the number of states that are set in routers for multicast groups. Aggregation is a natural solution to reducing the multicast forwarding states. In the way of sharing a common distribution tree among several groups, the approach to aggregated multicast reduces the number of forwarding states and improves network performance. Finding the best aggregation tree is an NP-complete problem, which requires approximate algorithms for the solution. Traditional greedy algorithm is not suitable for large scale network because of long computation time and low degree of aggregation. In this paper, an immune algorithm is proposed to solve the problem of aggregated multicast optimization. The immune approach achieves better solution by generating antibody of different antigens, which enables the algorithm to search in the vast space for the best solution. Simulations have shown that the immune algorithm has better performance than other algorithms in both the aggregation degree and the state reduction ratio.