2 research outputs found
GA-PSO-Optimized Neural-Based Control Scheme for Adaptive Congestion Control to Improve Performance in Multimedia Applications
Active queue control aims to improve the overall communication network
throughput while providing lower delay and small packet loss rate. The basic
idea is to actively trigger packet dropping (or marking provided by explicit
congestion notification (ECN)) before buffer overflow. In this paper, two
artificial neural networks (ANN)-based control schemes are proposed for
adaptive queue control in TCP communication networks. The structure of these
controllers is optimized using genetic algorithm (GA) and the output weights of
ANNs are optimized using particle swarm optimization (PSO) algorithm. The
controllers are radial bias function (RBF)-based, but to improve the robustness
of RBF controller, an error-integral term is added to RBF equation in the
second scheme. Experimental results show that GA- PSO-optimized improved RBF
(I-RBF) model controls network congestion effectively in terms of link
utilization with a low packet loss rate and outperform Drop Tail,
proportional-integral (PI), random exponential marking (REM), and adaptive
random early detection (ARED) controllers.Comment: arXiv admin note: text overlap with arXiv:1711.0635