Multi-objective Particle Swarm Optimization for Fuzzy Logic Based Active Queue Management
Multi-objective Particle Swarm Optimization for Fuzzy Logic Based Active Queue Management
复制标题
基于模糊逻辑主动队列管理的多目标粒子群优化
DOI：
10.1109/fuzzy.2006.1682010
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发表时间：
2006
期刊：
影响因子：
--
通讯作者：
D. Dawoud
中科院分区：
文献类型：
--
作者：
Clement N. Nyirenda;D. Dawoud
结合传统主动队列管理（AQM）算法和基于模糊逻辑的AQM算法的优点，提出了一种模糊逻辑拥塞检测（FLCD）算法。FLCD算法的隶属度函数（MF），然后自动设计使用多目标粒子群优化（MOPSO）算法，以实现最佳性能的所有主要性能指标的IP拥塞控制。将优化后的算法与基本的模糊逻辑主动队列管理算法和随机显式标记算法进行了比较。仿真结果表明，新方法提供了高的链路利用率，同时保持较低的抖动和分组丢失。与其基本变体和REM相比，新方法还具有更高的公平性和稳定性。
In this paper, a fuzzy logic congestion detection (FLCD) algorithm which synergically combines the good characteristics of traditional Active Queue Management (AQM) algorithms and fuzzy logic based AQM algorithms is proposed. The membership functions (MFs) of the FLCD algorithm are then designed automatically by using a Multi-objective Particle Swarm Optimization (MOPSO) algorithm in order to achieve optimal performance on all the major performance metrics of IP congestion control. The optimized algorithm is compared with the basic Fuzzy Logic AQM and the Random Explicit Marking (REM) algorithms. Simulation results show that the new approach provides high link utilization whilst maintaining lower jitter and packet loss. The new approach also exhibits higher fairness and stability compared to its basic variant and REM.