基于量子粒子群优化的主动队列管理新算法

New algorithm for active queue management based on quantum delta-potential-well-based particle swarm optimization

本文推导了基于流体流理论的网络简化模型,基于该模型将量子空间中的粒子群优化算法(QDPSO)应用于PID控制器参数优化,定义了一个综合调节时间、上升时间、超调量、系统静态误差、正弦跟踪误差等动静态性能指标函数,在给定的参数空间进行组合优化搜索,迅速求得获取使性能指标优化函数极小化的一组PID控制器参数,将PID控制器应用于网络主动队列管理系统中。仿真结果表明,在大时滞和突发业务流的冲击两种情况下,该方法设计的控制器的动静态性能优于PI算法,也优于GA、SPSO算法的优化结果,超调量均小于4%,调节时间均小于4 s,稳态误差均小于两个数据包。

A simplified network model based on fluid flow theory is derived in this paper, and based on this model, an improved algorithm, i.e. particle swarm optimization algorithm in quantum space is applied to the optimization of PID controller parameters. In the following, new performance functions including the system adjusting time, rise time, overshoot, steady state error and sinusoidal position tracking error are defined. A group of PID controller parameters that minimize the evaluation function can be calculated quickly by searching in the given controller parameter area, and then the PID controller is applied to AQM system. Simulation experimental results show that under the two conditions of large time delay and sudden business flow, the overshoot is less than 4% , the adjusting time is less than 4 seconds, and the steady error is less than 2 packets, so the dynamic state and steady state performances of the proposed algorithm are obviously superior to those of the existing PI algorithm, PID algorithm based on GA and PID algorithm based on standard PSO under the two conditions.