Abstract: This paper proposes an improved adaptive colony optimization algorithm and mainly presents a parameter of global best tour in strategies of pheromone updating to discusses QoS multicast routing problem with multiple constrains. The dynamic pheromone updating is adopted to ensure that global searching and convergence abilities are improved adaptively and avoid falling in local peak. Simulation results demonstrate that the proposed algorithm performs better than the combination of ant colony algorithm and genetic algorithm for solving QoS multicast routing problem with multiple constrains.

Key words: ant colony algorithm, QoS, multicast routing, adaptive

摘要： 提出一种改进的自适应蚁群优化算法，在信息素更新策略中引入全局最优系数，研究多约束条件下的QoS组播路由问题。动态更新信息素能够确保自适应地改进全局搜索能力和收敛性能，避免陷入局部最优解。仿真结果表明，该算法比蚂蚁-遗传算法在解决多约束条件下的QoS组播路由问题时更有效。

关键词: 蚁群算法, 服务质量, 组播路由, 自适应

CLC Number: 

• TP393