Optimization of RED-PID controller using the chaotic-subpopulation strategy-based Aquila and Math algorithms

Abstract

While the Transmission Control Protocol (TCP) is essential for congestion control by adjusting packet sending rates, it falls short of resolving the buffer bloat problem in critical routers. In response, Active Queue Management (AQM) mechanisms, notably Random Early Detection (RED), have been proposed to construct a feedback system, TCP/RED, for congestion control. However, existing AQM controllers like RED lack comprehensive optimization of control parameters for adapting to dynamic network conditions effectively. In this study, we propose a novel heuristic algorithm (AOMOA), which combines the global exploration of Aquila Optimizer (AO) with the local exploitation of Math Optimizer (MO), to optimize AQM controller parameters within the TCP/RED feedback system. AOMOA leverages chaotic-subpopulation and dynamic k-worst shift strategies to ensure a balance between exploration and exploitation, thereby mitigating premature convergence. Additionally, we analyze RED's intrinsic flaw and, therefore introduce a Proportional-Integral-Derivative (PID) adjuster into RED, RED-PID, to overcome the limitation according to theory analysis. To optimize RED-PID parameters, we present an optimization model ensuring stability and sensitivity in congestion control. Comprehensive simulations demonstrate that RED-PID, optimized by AOMOA, outperforms the standard RED controller, showcasing superior congestion control performance.