基于多目标遗传算法优化弹性光网络的多路径保护机制

摘要

Multipath provisioning algorithm outperforms single-path provisioning algorithm in terms of bandwidth blocking probability. However, multipath transmission causes the differential delay among different paths and affects the usage of spectrum resources. To address the problem, a Genetic Multipath Protection Algorithm (GMPA) is proposed based on multi-objectives genetic algorithm. according to traffic requests, the K link-disjoined paths and bandwidth assignments are designed as the population initialization scheme. A vector function is proposed to balance the path-distance difference and network spectrum resources by optimizing population classification and crowding distance sorting. An individual self-cross pattern is introduced and the variation range and constraint conditions of bandwidth gene are designed to improve the algorithm search ability and convergence. Compared with the Multiple Path Protection (MPP) and Primary First-fit Modified Backup Last-fit (PF-MBL), simulation results show that the proposed GMPA algorithm can get lowest bandwidth blocking probability, its spectrum resource utilization is close to the optimal MPP, and the path-distance difference of GMPA is better than that of MPP.%弹性光网络中多路径的保护方案相比单路径有效地降低网络带宽阻塞率，但会导致接收端多径时延差的问题，且业务的多路径分割传输策略使用了光网络较多的频谱资源。该文基于多目标遗传算法提出了遗传多路径保护算法(Genetic Multipath Protection Algorithm, GMPA)，解决多路径时延差和节约频谱资源问题。在GMPA算法中，根据业务请求在光网络中建立K条边分离最短路径和带宽分配方案作为GMPA算法的初始种群，设计了一种联合考虑传输时延差和带宽资源分配的向量函数优化种群分类和拥挤距离排序。为提高算法的搜索能力和收敛速度，算法在交叉操作中设计个体自交叉方式，在变异过程中设置了带宽基因位变异范围及约束条件。仿真结果表明，相比多路径保护(Multiple Path Protection, MPP)算法和工作路径首次分配保护路径最后分配(Primary First-fit Modified Backup Last-fit, PF-MBL)算法，GMPA算法获得最低的带宽阻塞率，其频谱资源利用率接近最优的MPP算法，路径间距离差异性能优于MPP算法。