Multi-objective Particle Swarm Optimization for Fuzzy Logic Based Active Queue Management

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.