TY - EJOUR
AU - Sheikhan, Mansour
AU - Hemmati, Ehsan
AU - Shahnazi, Reza
PY - 2024
DA - February
TI - GA-PSO-Optimized Neural-Based Control Scheme for Adaptive Congestion Control to Improve Performance in Multimedia Applications
T2 - Majlesi Journal of Electrical Engineering
VL - 6
L1 - https://oiccpress.com/Majlesi-Journal-of-Electrical-Engineering/article/ga-pso-optimized-neural-based-control-scheme-for-adaptive-congestion-control-to-improve-performance-in-multimedia-applications/
N2 - 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 outperforms Drop Tail, proportional-integral (PI), random exponential marking (REM), and adaptive random early detection (ARED) controllers.
IS - 1
PB - OICC Press
KW - Cloud computing, DDoS attacks, Machine Learning, deep learning techniques, . ,, IAU-South Tehran Branch 
EN -