Non-linear current versus voltage (I-V) characteristics make PV modelling problematic. Optimization strategies are best for detecting non-linear equation parameters. While various optimising algorithms are used to predict solar-PV characteristics, the ideal results remain elusive. This publication proposes a hybrid approach, Chaotic Grey Wolf Optimization (c-GWO), to estimate double diode (DD) solar PV parameters. The framework’s precision and convergence time are compared to Ant Colony Optimization (ACO), and Rat Swarm Algorithm (RSA) for the solar PV model with two diodes. The proposed outcomes indicate that the hybrid algorithm may anticipate precise optimal values with minimal iteration in different environments. Different mistakes are calculated after examining double diode solar PV model parameter estimation. To evaluate algorithm precision, a non-parametric test is run. The findings indicate that the hybrid method surpasses the other algorithms presented in the article.