## 5 Conclusions
 & **DE/BBO** & **BLPSO** & **CLPSO** & **MSSA** \\ \hline SD & \(3.13\times 10^{-6}\) & \(2.43\times 10^{-5}\) & \(1.48\times 10^{-7}\) & \(7.80\times 10^{-5}\) & \(2.94\times 10^{-4}\) & \(3.63\times 10^{-4}\) & \(1.78\times 10^{-4}\) & \(1.44\times 10^{-4}\) & \(1.49\times 10^{-6}\) \\ Mean & \(9.86\times 10^{-4}\) & \(9.98\times 10^{-4}\) & \(9.84\times 10^{-4}\) & \(1.02\times 10^{-3}\) & \(1.55\times 10^{-3}\) & \(1.56\times 10^{-3}\) & \(1.48\times 10^{-3}\) & \(1.15\times 10^{-3}\) & \(9.94\times 10^{-4}\) \\ Max & \(1.00\times 10^{-3}\) & \(1.08\times 10^{-3}\) & \(9.87\times 10^{-4}\) & \(1.34\times 10^{-3}\) & \(2.26\times 10^{-3}\) & \(2.40\times 10^{-3}\) & \(1.74\times 10^{-3}\) & \(1.55\times 10^{-3}\) & \(9.99\times 10^{-4}\) \\ Min & \(9.83\times 10^{-4}\) & \(9.83\times 10^{-4}\) & \(9.82\times 10^{-4}\) & \(9.83\times 10^{-4}\) & \(1.01\times 10^{-3}\) & \(1.03\times 10^{-3}\) & \(1.06\times 10^{-3}\) & \(9.99\times 10^{-4}\) & \(9.83\times 10^{-4}\) \\ \hline
**Algorithm** & **TLABC** & **GOTLBO** & **STLBO** & **IJAYA** & **SATLBO** & **MVO** & **QMYO** & **Rcr-JADE** & **IWOA** \\ \hline SD & \(3.19\times 10^{-17}\) & \(4.37\times 10^{-17}\) & \(1.38\times 10^{-5}\) & \(5.96\times 10^{-17}\) & \(3.55\times 10^{-7}\) & \(2.93\times 10^{-5}\) & \(1.37\times 10^{-5}\) & \(2.58\times 10^{-5}\) & \(1.75\times 10^{-5}\) \\ Mean & \(2.43\times 10^{-3}\) & \(2.43\times 10^{-3}\) & \(2.43\times 10^{-3}\) & \(2.43\times 10^{-3}\) & \(2.43\times 10^{-3}\) & \(2.46\times 10^{-3}\) & \(2.44\times 10^{-3}\) & \(2.45\times 10^{-3}\) & \(test, the performance of the algorithms was compared and determined. The results of these tests indicated the superiority of the ICO compared with other competitive algorithms in terms of accuracy, reliability, good convergence speed, and computation. Moreover, the further improvement made in the CO revealed that the ICO was able to significantly reduce the computing time by maintaining or improving its features, and it also demonstrated enhanced performance. The ICO and CO achieved the best sum rank results among the 12 applied algorithms for the studied PV models. However, the ICO algorithm reduced the computation time of the CO by approximately 40%. It is also worth noting that the ICO's convergence speed was high, reaching an optimal solution within a few thousand function evaluations in most cases. Accordingly, the ICO can be considered a promising candidate method for extracting the model parameters of PV cells and modules. Hence, the ICO will be applied in our future studies to solve a variety of power systems' operation and planning optimization problems, including placement of renewable distributed generations, economic load dispatch, and feeder reconfiguration. Furthermore, we intend to develop binary and multiobjective versions of the ICO algorithm. All the authors contributed to formulating the research idea, algorithm design, result analysis, and writing and reviewing the research. All authors have read and agreed to the published version of the manuscript. This work was funded by Ajman University under internal research grant 2022-IRG-ENIT-9. Not applicable. Data Availability StatementData sharing is not applicable as no new data were generated. The authors declare no conflict of interest.