Abstract
Faced with technological advances and increasing global energy demand, solar energy is positioning itself as a sustainable, clean, and secure solution, occupying a key role in contemporary energy systems. This article proposes a mixed approach to determine the parameters of a SY-M80W photovoltaic panel. The proposed technique leverages the global exploration capability of the ant colony algorithm (ACOR) in the continuous domain to generate relevant candidate solutions, while the specific swarm optimization algorithm (PSO) plays a role in the local exploitation phase to refine the search for the best solutions. This collaboration helps overcome the individual constraints of each technique, particularly deadlock or anticipated convergence. The primary goal is to optimize the accuracy, consistency, and convergence speed in estimating the five essential parameters of the diode model. Validation was performed through simulations using experimental data from tests conducted under standard environmental conditions. The results indicate that the ACOR–PSO technique provides more accurate identification, increased stability, and acceptable convergence time compared to conventional methods or a single algorithm. This approach is therefore encouraging for modeling, simulation, control, and energy optimization applications in the photovoltaic sector.

Key words: Ant colony, swarm optimization, photovoltaic, optimization, identification