Abstract Parameter estimation for solar photovoltaic (PV) models is a challenging issue due to the complex nonlinear multivariable of the current‐voltage and power‐voltage characteristics. In this article, an improved heap‐based algorithm (IHBA) is proposed to improve the performance of a recently published algorithm called heap‐based algorithm (HBA). The HBA's performance is enhanced by applying an effective exploitation feature to boost the searching around the leader position with the goal of enhancing its global search capabilities and avoiding becoming trapped in a local optimum. The proposed IHBA and the standard HBA are developed considering the practical limits of the electrical parameters of PV models to minimize the root mean square error (RMSE) between the experimental and simulated results. The numerical analyses for the PVM‐752GaAs PV module including single‐diode model (SDM), double‐diode model (DDM) and triple‐diode model (TDM) are investigated to estimate five, seven, and nine electrical parameters of these models, respectively. Besides, different recent optimization techniques are simulated with fair comparisons, which are forensic‐based investigation (FBI), equilibrium optimizer (EO), Jellyfish Search (JFS), HBA, Marine Predator Algorithm (MPA) and Enhanced MPA (EMPA), and are compared with the proposed IHBA. Several separate runs are illustrated for the proposed IHBA in comparison with others, whereas the whisker box plot and T‐tests are activated to evaluate their effectiveness metrics. The simulation results derive higher superiority of the proposed IHBA with the minimum RMSE objective and standard deviation of (0.000228 and 3.7 × 10 6 ), (0.000184 and 1.93 × 10 5 ) and (0.000017 and 2.11 × 10 5 ) for SDM, DDM, and TDM, respectively, with respect to the standard HBA, other recent and reported optimization techniques. Additionally, the P‐indicator is applied in this study to illustrate the evidence for the alternative hypothesis. Small values of the P‐indicator for the proposed IHBA are obtained compared to the standard HBA and other recent optimization techniques, where IHBA achieves p ‐value of 1.0336 × 10 17 , 4.037 7× 10 11 and 2.1490 × 10 10 for SDM, DDM and TDM, respectively. The H‐value is always one which elucidates that the hypothesis probability is not more than 5% for all algorithms for SDM, DDM and TDM. Moreover, the proposed IHBA demonstrates higher efficiency as it provides the first ranks in the confidence interval values compared with other algorithms. Furthermore, the proposed IHBA is significantly employed for the SQ_150 PV module considering the TDM with diverse solar irradiance and temperatures, where significant closeness between the emulated and experimental P – V curves is evidently demonstrated.