基于NSGAII算法的多目标参数优化的主动队列管理新策略（学术论文）.doc

-  PAGE 10 - 基于NSGA-II算法的多目标参数优化的主动队列管理新策略收稿日期： 基金项目：国家自然科学基金, 江苏省“六大人才高峰”项目（07-E-013）,南通市应用研究计划项目（K2007004） 陆锦军1,2李志权2王执铨1 (1南京理工大学自动化学院 南京 210094; 2南通职业大学现代教育技术中心 南通 226007) 摘 要 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NSGA-II与PGA相结合的优化算法应用于PID控制器参数优化，提出了一种多目标PID优化设计方法——在满足系统鲁棒性的前提下，以超调量、上升时间和调整时间最小作为多目标优化的子目标，并将NSGA-Ⅱ与PGA相结合对其求解。该算法求得的Pareto最优解分布均匀，收敛性和鲁棒性好，根据网络主动队列管理控制系统的要求在Pareto解集中选择最终的满意解。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???控制器的动静态性能优于RED、GA、SPSO、QDPSO算法的优化结果。 关键词 主动队列管理 网络拥塞 PID控制 NSGA-II 中图分类号 TP273 文献标志码 A 国家标准学科分类代码 120.30 A New Tactics of Multi-Object Parameter Optimization for Active Queue Management Based on NSGA-II Algorithm LU Jin-jun1,2 LI Zhi-quan2 WANG Zhi-quan1 （1School of Automation, Nanjing University of Science and Technology,Nanjing210094, China; 2 Center of Education and Technology, Nantong Vocational College, Nantong 226007,China） Abstract: Simplified network model based on fluid flow theory is derived in this paper, and based on this model, an improved algorithm, i.e. optimization algorithm combining NSGA-II and PGA is applied to optimization of PID controller parameters. In the following, a multi-object PID optimization design method is put forward, i.e. when robustness of the system is satisfied, the minimum of overshoot, rise time and adjusting time is taken as the sub-object of multi-object optimization, and solve it by combining NSGA-II and PGA. The Pareto optimal solution got by this algorithm distributes even, and has good convergence and robustness. According to request of networked Active Queue Management control system, a satisfying solution is chosen in Pareto solution set. The simulation experimental results show that under the two conditions of large time delay and sudden business flow, the dynamic state and steady state performances of the proposed algorithm are obviously superior to those of the existing RED, GA, SPSO and QDPSO algorithms. Key words: active queue management; network congestion; PID control; NSGA-II