基于人工鱼群优化的光网络攻击感知路由算法

An attack-aware routing algorithm for optical networks based on artificial fish swarm optimization

针对光网络中的安全路由问题,通过采用MaxLAR(最大光路攻击半径)准则,运用人工鱼群算法的快速搜索和全局寻优能力,提出了一种新的基于人工鱼群优化的光网络攻击感知路由算法。该算法可找出在给定条件下光路中最小的MaxLAR,可增强光网络的攻击预防能力,减少潜在物理层攻击对光路造成的可能损伤。算法分析及Benchmark测试函数实验表明,该算法是可行的,且取得了较好的性能。

In respect of the security routing in optical networks,this paper proposes a new attack-aware routing algorithm for optical networks based on artificial fish swarm optimization in compliance with the Maximum Light path Attack Radius （Max-LAR）criterion,using the fast search and global optimization ability of the artificial fish swarm algorithm.This algorithm can find out the smallest maximum attack radius in light paths under given conditions,enhance the attack prevention ability of opti-cal networks and minimize the possible damage to light paths caused by the potential physical-layer attacks.Algorithm analysis and Benchmark test function experiments indicate that this algorithm is feasible and has good performance.