Optimization of RED-PID Chaotic-Subpopulation Strategy-based Aquila and Math Algorithms

The Transmission Control Protocol (TCP) plays a crucial role in congestion control by adjusting packet send-ing rates, but it falls short of addressing the buffer bloat issue in critical routers. To mitigate this, Active Queue Management (AQM) mechanisms like Random Early Detection (RED) have been introduced to form a TCP/ RED feedback system for congestion control. However, by analyzing the magnitude-frequency characteristic of TCP/RED, this paper finds it has sluggish response time and slowly stabilizes in congestion control. Therefore, this paper presents a novel AQM controller named RED-PID, which integrates a Proportional-Integral-De-rivative (PID) adjustor into RED, enhancing the control structure. Furthermore, frequency domain analysis provides the stability criteria and parameters for TCP/RED-PID. Given the lack of a special optimization of control parameters for adapting to TCP/RED-PID effectively, this paper introduces a novel heuristic algorithm (AOMOA), which combines the global exploration strengths of the Aquila Optimizer (AO) with the local exploitation capabilities of the Math Optimizer (MO). Meanwhile, a chaotic-subpopulation strategy is proposed,utilizing two subpopulations simultaneously to fasten the converging speed. Moreover, the dynamic k-worst shift is introduced to strike a balance between global exploration and local exploitation across both optimizers. The TCP/RED-PID model was analyzed and validated using the NS3 simulator. Comprehensive simulations demonstrate that RED-PID, optimized by AOMOA, significantly outperforms the standard RED controller, ex-hibiting superior congestion control performance.