Optimization of PID Controllers Using Groupers and Moray Eels Optimization with Dual-Stream Multi-Dependency Graph Neural Networks for Enhanced Dynamic Performance

Traditional proportional–integral–derivative (PID) controllers are often utilized in industrial control applications due to their simplicity and ease of implementation. This study presents a novel control strategy that integrates the Groupers and Moray Eels Optimization (GMEO) algorithm with a Dual-Stream Multi-Dependency Graph Neural Network (DMGNN) to optimize PID controller parameters. The approach addresses key challenges such as system nonlinearity, dynamic adaptation to fluctuating conditions, and maintaining robust performance. In the proposed framework, the GMEO technique is employed to optimize the PID gain values, while the DMGNN model forecasts system behavior and enables localized adjustments to the PID parameters based on feedback. This dynamic tuning mechanism enables the controller to adapt effectively to changes in input voltage and load variations, thereby enhancing system accuracy, responsiveness, and overall performance. The proposed strategy is assessed and contrasted with existing strategies on the MATLAB platform. The proposed system achieves a significantly reduced settling time of 100 ms, ensuring rapid response and stability under varying load conditions. Additionally, it minimizes overshoot to 1.5% and reduces the steady-state error to just 0.005 V, demonstrating superior accuracy and efficiency compared to existing methods. These improvements demonstrate the system’s ability to deliver optimal performance while effectively adapting to dynamic environments, showcasing its superiority over existing techniques.