In order to determine the unknown parameters of the photovoltaic (PV) model, this paper introduces a novel evolutionary hybrid optimization technique that combines the single candidate optimizer (SCO) and the chaotic sand cat optimizer (CSCO). The addition of a chaotic structure to the SCO approach improves its capacity to explore while hindering earlier convergence. The effective global search capability of the CSCO and the effective local search capability of the SCO technique is credited with the effectiveness of the suggested hybrid strategy, known as the CSCSC. The effectiveness of the CSCSC algorithm is assessed using mathematical test functions, and the outcomes are contrasted with those of the conventional SCO and a number of efficient optimization methods. Following that, the CSCSC method is used to obtain the PV parameters. It is said that finding these parameters is an objective function whose differences between estimated and experimental data should be kept to a minimum. To assess how well the CSCSC obtains parameters, the single diode, double diode, and PV module models are employed. Based on the results of the numerical testing, it can be concluded that the newly presented algorithm performs better than previously described approaches in the academic literature when it comes to producing optimal solutions. The simulation findings demonstrate that the novel optimization procedure, which has the lowest root mean square error, offers better optimal solutions than earlier techniques for all varieties of solar cells.