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Active queue control aims to improve the overall communication networkthroughput while providing lower delay and small packet loss rate. The basicidea is to actively trigger packet dropping (or marking provided by explicitcongestion notification (ECN)) before buffer overflow. In this paper, twoartificial neural networks (ANN)-based control schemes are proposed foradaptive queue control in TCP communication networks. The structure of thesecontrollers is optimized using genetic algorithm (GA) and the output weights ofANNs are optimized using particle swarm optimization (PSO) algorithm. Thecontrollers are radial bias function (RBF)-based, but to improve the robustnessof RBF controller, an error-integral term is added to RBF equation in thesecond scheme. Experimental results show that GA- PSO-optimized improved RBF(I-RBF) model controls network congestion effectively in terms of linkutilization with a low packet loss rate and outperform Drop Tail,proportional-integral (PI), random exponential marking (REM), and adaptiverandom early detection (ARED) controllers.

GA-PSO-Optimized Neural-Based Control Scheme for Adaptive Congestion Control to Improve Performance in Multimedia Applications