*3.10. Others*

New methods usually lead to breakthroughs in specific problems, since they bring different search mechanisms. Therefore, researchers favor novel approaches and their variants in exploring the PV model parameter extraction, and have provided some new approaches.

Naeijian et al. [135] developed a Whippy Harris Hawk Optimization (WHHO) that handled the worst individual by adding elimination cycles to improve all-around performance. The simulation results demonstrated the fast convergence of WHHO and the high robustness and accuracy for the extracted parameters. Xiong et al. [4] used a Gaining-Sharing Knowledge-based algorithm (GSK) for the issue addressed in this work for the first time. They demonstrated the high accuracy, robustness, and competitiveness of GSK in different PV models. Sallam et al. [136] developed an improved GSK (IGSK) using a boundary constraint processing mechanism, a linear population size reduction technique, and knowledge rate adaptive technology. Xiong et al. [137] applied Supply and Demand Based Optimization (SDO) and pioneered a comparison between SDO and several advanced methods in extracting PV model parameters, which powerfully demonstrated the feasibility and competitiveness of SDO. Diad et al. [138] used a Tree Growth Algorithm (TGA) to tackle the issue, and the RMSE values showed the TGA's good accuracy. Abbassi et al. [139] provided PV model parameters extracted by a Salp Swarm Algorithm (SSA) and demonstrated its accuracy and competitiveness with multiple metrics. Sharma et al. [140] solved this problem using Tunicate Swarm Algorithm (TSA) and verified TSA's accuracy,

feasibility, and competitiveness with simulations. Gupta et al. [141] designed a chaotic TSA (CTSA) to tackle the issue, and the results supported its accuracy and competitiveness. Ramadan et al. [142] developed Chaotic Game Optimization (CGO) for the issue and confirmed its good performance. Long et al. [143] designed a Hybrid Seagull Optimization (HSOA) with three mechanisms, differential mutation, memory-guided and non-linear control, and tested it in different PV models. Shaban et al. [144] employed Rungakuta Optimizer (RUN) to tackle the issue. The simulation results demonstrated RUN's excellent competitiveness, convergence, and robustness. In [145], the authors used a Flower Pollination Optimization Algorithm (FPOA) for the TDM's parameters with industrial samples. The results supported the high-performance of FPOA in the TDM. In [146], the authors used the Symbiotic Organisms Search (SOS) method to tackle the issue. The results powerfully demonstrated the superiority of SOS.




**Table 17.** Hybrids' experiment results.

The "N/A" means that there is insufficient data to support an average algorithm ranking using the Friedman Test on the three cases: SDM, DDM, and Photowatt-PWP201.

Most of the above methods are applications of newly proposed metaheuristics in recent years, and their essential information and experimental results are summarized in Tables 18 and 19. SSA has the smallest TNFES, followed by IGSK, RUN, GSK, SDO, TSA, HSOA, CTSA, SOS, WHHO, and TGA. WHHO and TGA achieve the same combined MIN RMSE ranking, followed by GSK, IGSK, HSOA, and SOS, in that order. It is worth noting that RUN, as the original algorithm, obtained more accurate parameter values with not many computational resources. TGA achieved the most efficient MIN RMSE values for DDM and TDM, and GSK received enough accuracy to compare with many advanced algorithms with not many computational resources. This suggests that exploring the application of new methods may make it easier to achieve a solution to the issue.


**Table 18.** Other methods' essential information and metrics.


**Table 19.** Other methods' experiment results.

The "N/A" means that there is insufficient data to support an average algorithm ranking using the Friedman Test on the three cases: SDM, DDM, and Photowatt-PWP201.
