A Hybrid Metaheuristic Optimization Approach for Efficient PID Parameter Tuning in Fuel Cell Power Systems

This paper presents a novel hybrid metaheuristic algorithm, termed Pheromone-Tabu Enhanced Snake Optimizer (PTESO), for the optimal tuning of PID controller parameters in fuel cell power systems integrated with a boost converter. The proposed approach combines the global search capability of the Snake Optimizer (SO) with a pheromone-based learning mechanism inspired by Ant Colony Optimization (ACO) and a dynamic memory structure derived from Tabu Search (TS). The pheromone-guided strategy improves information sharing among candidate solutions, while adaptive Gaussian mutation enhances population diversity and prevents premature convergence. Furthermore, the integration of tabu memory and aspiration criteria strengthens local search performance and avoids revisiting previously explored regions. Additional mechanisms, including elite-guided exploration, adaptive mating, dynamic fighting behavior, and stagnation escape strategies, are incorporated to achieve a balanced exploration-exploitation process. The proposed PTESO algorithm is employed to optimize PID controller gains with the objective of improving transient response characteristics, reducing overshoot, minimizing settling time, and enhancing voltage regulation and overall system stability. The results demonstrate the effectiveness of PTESO as a reliable and robust optimization framework for advanced control applications in fuel cell power systems.

کلیدواژه ها:

fuel cell ، boost converter ، PID controller