基于粒子群优化的主动队列管理方法

Active queue management algorithm based on particle swarm optimization

针对网络拥塞现象,基于粒子群优化(PSO)提出了一种新的主动队列管理算法RQQM。该算法首先通过粒子群优化和变异算子来计算当前队列长度,并且基于到达速率和当前队列长度给出了丢包策略和丢包概率。最后,以实际数据将RQQM算法与基于速率的早期检测公平队列管理(RFED)算法和自适应主动队列管理(ABLUE)算法进行仿真实验,发现丢包率受利用率和缓冲区影响较大;同时实验结果表明RQQM算法的公平性远远优于其他两种算法,其平均丢包率降低至12.21%。

In order to mitigate the network congestion, a new active queue management algorithm named RQQM （ Rate and Queue-based Queue Management） was proposed by Particle Swarm Optimization （PSO）. In this algorithm, the actual queue length was deducted with PSO and variation factor, and the dropping strategy and dropping rate were given based on arrival rate and actual queue length. Then, a simulation with actual data was conducted to compare the algorithm performance between RQQM algorithm and RFED （ Rate-based Fair Early Detection） algorithm, as well as ABLUE （ Adaptive BLUE） algorithm. The results show that the dropping rate is greatly influenced by the utilization rate and buffer size, and the fairness of RQQM is much better than that of the other two algorithms, its average packet loss rate is decreased to 12.21%.