Genetic Algorithm Fitness Function Comparison for PID Controller Optimization in DC–DC Boost Converters

Abstract

DC-DC boost converters are extensively employed in power supply and renewable energy applications. Maintaining a stable output voltage under changing input and load conditions is a major difficulty in such systems. Among different control approaches, the Proportional–Integral–Derivative (PID) controller remains the most widely applied due to its simple structure and reliable performance. However, selecting the optimal set of PID gains that balance steady-state accuracy and transient response is often complex. In this study, a Genetic Algorithm (GA) is employed to optimize the PID parameters of a boost converter using four different fitness functions: Integral of Time-weighted Absolute Error (ITAE), Integral of Absolute Error (IAE), Integral of Time-weighted Squared Error (ITSE), and Integral of Squared Error (ISE). MATLAB/Simulink has been utilized to model the proposed converter to evaluate the dynamic performance achieved by each optimization criterion. Simulation results illustrate that the choice of fitness function has a significant effect on the converter’s transient and steady-state behavior. Among the tested criteria, the ITAE-based tuning yields the best performance, providing minimal overshoot, faster rise time, and reduced steady-state error.