动态RWA( 路由与波长分配) 问题是智能光网络的核心问题, 以蚁群算法为代表的启发式算法是解决此类问题的优选方案之一。文章提出一种基于ADACO(自适应蚁群优化)算法的RWA机制, 针对信息素挥发系数ρ采取自适应策略, 实现了对全局信息素更新态调节。理论分析和数值仿真结果表明, 与Dijkstra+FF(首次命中)算法相比, 改进的ADACO+FF算法可以有效地降低网络阻塞率, 规则型Mesh网络和NSFNET(国家科学基金会网络)的阻塞率最高分别降低了0.3和0.2。

Dynamic Routing and Wavelength Assignment (RWA) is a key issue to Intelligent Optical Networks (ION), and heuristic algorithms represented by the Ant Colony Optimization (ACO) algorithm is one of the most preferred schemes for such issues. This paper proposes an Adaptive Ant Colony Optimization (ADACO) algorithm-based RWA mechanism, which realizes state adjustment for the global pheromone updating by adopting adaptive strategies. Theoretical analysis and numerical simulation results show that compared with Dijkstra+FF algorithm, the improved ADACO+FF algorithm effectively lowers the network blocking probability and the optimal blocking probability improvement in regular MESH and NSFNET topology scenario is up to 0.3 and 0.2, respectively.