基于改进蚁群算法的光资源路径规划

Optical Resource Path Planning Based on Improved Ant Colony Algorithm

传统光资源路径选择方法依赖随机或就近原则,导致光交箱端口使用率极度不平衡,部分光交箱端口占用过度饱和,引发光信号衰弱影响用户体验,而另一部分光交箱端口资源却鲜有利用。本文呈现了一种新的基于改进的蚁群算法的光网络资源路径分配策略。为了解决常规蚁群算法在寻优过程中的慢速收敛问题及易陷入局部最优的困境,本文提出基于改进的Metropolis接受准则和路径交叉策略的蚁群优化算法。实验仿真结果表明,该方法能根据使用者设定的不同目标侧重,生成多条符合设定侧重的路径,满足了不同光路连接需求,提升了光交箱端口整体的合理利用率,从而实现资源的充分利用,减少运营商的消耗成本。同时,在改进算法与经典的蚁群算法的对比实验中,也验证了改进算法的有效性和可行性。

Traditional methods for optical resource path selection,relying on random or proximity principles,lead to severe imbalances in the utilization of optical cross-connect(OXC)ports.Some ports are oversaturated,causing light signal attenuation that degrades user experience,while others are underutilized.This paper presents a novel optical network resource path allocation strategy based on an improved ant colony optimization(ACO)algorithm.To overcome the issues of slow convergence and susceptibility to local optima in the conventional ACO,an improved ACO algorithm was proposed by using the Metropolis acceptance criteria and path crossover strategies.Simulation results demonstrate that our method can generate multiple paths that align with user-specified priorities,meeting various optical link requirements,enhancing the overall rational utilization of OXC ports,fully utilizing resources,and reducing operator costs.Comparative experiments with the classic ACO also confirm the effectiveness and feasibility of our improved algorithm.