关键词: 蚁群算法, QoS组播路由, 信息素, 混沌

Abstract:

The ant colony algorithm is easy to fall into local optimum and its convergent speed is slow in solving multiple QoS constrained multicast routing problems. Therefore, an adaptive pheromone updating ant colony algorithm was proposed to solve the problems. First, chaos perturbation was used to improve nodes selection strategy and evaporation coefficient was adjusted dynamically according to the intensity of pheromone trail,which improved the global search ability.Second, the pheromone trail on the path was updated adaptively according to the solution in the algorithm. The convergence performance was significantly improved by this algorithm. According to the simulations, under the same experimental conditions,the basic ant algorithm converged to local optimal cost of 87 in 12 iterations, and the multi-behaved ant colony algorithm in combination with quantum-behaved particle swarm optimization converged to local optimal cost of 66 in 7 iterations.The algorithm converged to global optimal cost of 62 in 10 iterations, which showed that the algorithm was better than the two previous algorithms.

Key words:  ant colony algorithm, QoS multicast routing, pheromone, chaos