The tuning of proportional-integral (PI) controllers plays a crucial role in achieving optimal performance in closed-loop control systems. This abstract presents a novel approach for tuning PI controllers in a closed-loop single-switched semi-quadratic buck converter using genetic and differential evolution algorithms. The semi-quadratic buck converter is a widely used power electronics topology with numerous applications in voltage regulation and power management. The proposed approach leverages the strengths of both genetic and differential evolution algorithms to search for the optimal values of PI controller parameters. The genetic algorithm employs the principles of natural selection, crossover, and mutation to evolve a population of candidate solutions over successive generations. On the other hand, the differential evolution algorithm uses a strategy of vector differences and perturbations to explore the solution space efficiently. The objective of the tuning process is to minimize a predefined performance criterion, such as settling time, overshoot, or steady-state error, while ensuring stability and robustness of the closed-loop system. The PI controller parameters are encoded as genes in the chromosome representation, and the fitness of each individual in the population is evaluated using simulation-based objective functions. The utilization of genetic and differential evolution algorithms for tuning PI controllers in closed-loop single-switched semi-quadratic buck converters offers an effective and efficient approach to achieve optimal performance. The proposed method contributes to the advancement of power electronics control techniques, enabling better utilization of energy and improved overall system performance in various applications.