In MATLAB, implementing a Golden Eagle Algorithm (GEA)-optimized PID controller involves defining an objective function that represents the system's performance based on PID parameters. The objective function simulates the closed-loop system response using a transfer function and the PID controller with the current parameter set. The GEA is then employed to optimize the PID parameters by iteratively updating the eagle positions in the search space, where each position corresponds to a potential set of PID parameters. The fitness of each eagle's position is evaluated using the objective function, and the algorithm aims to find the optimal parameters that minimize the cost function. The GEA process is repeated until convergence, resulting in a PID controller tuned for the specific control system. This approach allows for the automated tuning of PID parameters, optimizing the controller's performance for a given dynamic system.