基于量子粒子群优化的网络拥塞控制新策略

为了改善网络拥塞控制系统的性能,基于流体流理论的网络简化模型,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2种情况下,该方法设计的控制器的动静态性能优于RED,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4s,稳态误差均小于2个数据包.

New algorithm of network congestion control based on quantum delta-potential-well-based particle swarm optimization

To improve the performance of the network congestion control system,based on simplified network model of fluid flow theory,an improved algorithm,i.e.particle swarm optimization algorithm in quantum space is applied to optimization of PID(proportion integral derivative) controller parameters.A new performance function including the system adjusting time,rise time,overshoot,steady state error and sinusoidal position tracking error is defined.A group of PID controller parameters that minimize the evaluation function is calculated rapidly by searching in the given controller parameter area,and then the PID controller is applied to AQM(active queue management) system.The simulation experiment results show that under the two conditions of large time delay and sudden business flow,the overshoot is both cases are less than 4%,the adjusting time less than 4 seconds,and the steady error less than 2 packets.Therefore the dynamic state and steady state performances of the proposed algorithm are obviously superior to those of the existing RED(random early detection),PI(proportion integral) algorithms,and also superior to the optimization results from GA(genetic algorithm) and PSO(particle swarm optimization) algorithms.