Adaptive active queue management controller for TCP communication networks using PSO-RBF models

Addressing performance degradations in end-to-end congestion control has been one of the most active research areas in the last decade. Active queue management (AQM) is a promising technique to congestion control for reducing packet loss and improving network utilization in transmission control protocol (TCP)/Internet protocol (IP) networks. AQM policies are those policies of router queue management that allow for the detection of network congestion, the notification of such occurrences to the hosts, and the adoption of a suitable control policy. Radial bias function (RBF)-based AQM controller is proposed in this paper. RBF as a nonlinear controller is suitable as an AQM scheme to control congestion in TCP communication networks since it has nonlinear behavior. Particle swarm optimization (PSO) algorithm is also employed to derive RBF output weights such that the integrated-absolute error is minimized. Furthermore, in order to improve the robustness of RBF controller, an error-integral term is added to RBF equation. The output weights and the coefficient of the integral error term in the latter controller are also optimized by PSO algorithm. It should be noted that in both proposed controllers the parameters of radial basis functions are selected to symmetrically partition the input space. The results of the comparison with adaptive random early detection (ARED), random exponential marking (REM), and proportional-integral (PI) controllers are presented. Integral-RBF has better performance not only in comparison with RBF but also with ARED, REM and PI controllers in the case of link utilization while packet loss rate is small.