基于改进的免疫克隆蛙跳算法的多约束QoS路由优化研究

Multi-constraints QoS routing optimization based on improved immune clonal shuffled frog leaping algorithm

针对多约束路由选择问题,设计了数学模型并提出了一种改进的免疫克隆蛙跳算法。所提方法结合了免疫克隆算法与传统蛙跳算法,在分组丢失率、链路带宽、时延抖动、时延、能量损耗条件的限制下,计算源节点到终端节点的能量损耗,通过所提算法寻找一条能量损耗最小的路径。在仿真实验中,将所提算法与自适应遗传算法、自适应蚁群算法进行了对比。实验结果表明,所提算法在一定程度上解决了多约束QoS单播路由优化问题,与自适应遗传算法与自适应蚁群算法相比,所提算法避免了局部最优,有效地降低了数据在传输路径上的能量损耗。

Aiming at the multi-constraint routing problem,a mathematical model was designed,and an improved immune clonal shuffled frog leaping algorithm(IICSFLA)was proposed,which combined immune operator with traditional SFLA.Under the constraints of bandwidth,delay,packet loss rate,delay jitter and energy cost,total energy cost from the source node to the terminal node was computed.The proposed algorithm was used to find an optimal route with minimum energy cost.In the simulation,the performance of IICSFLA with adaptive genetic algorithm and adaptive ant colony optimization algorithm was compared.Experimental results show that IICSFLA solves the problem of multi-constraints QoS unicast routing optimization.The proposed algorithm avoids local optimum and effectively reduces energy loss of data on the transmission path in comparison with adaptive genetic algorithm and adaptive ant colony optimization algorithm.