###### Abstract
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**Authors** & **Years** & **Algorithms** & **Control Parameter** & **Remark** \\ \hline
[12] & 2019 & Improve FPA & Duty cycle & The proposed technique merged fractional chaos maps with the Flower Pollination Algorithm (FPA) and compared the accuracy between different chaotic variants. \\ \hline
[13] & 2021 & CS & Duty cycle & The proposed algorithm avoids spreading the initial particles among the whole curve to predict shading patterns. \\ \hline
[14] & 2017 & FPA & Duty cycle & FPA was added to the MPPT application and the efficiency of FPA was compared with P\&O and INC. \\ \hline
[15] & 2021 & Improve GWO & Duty cycle & Levy flight strategy was added to GWO to improve MPPT tracking accuracy. \\ \hline
[16] & 2021 & TSA & Voltage & The TSA algorithm was used to solve the partial shadow problem in MPPT, and a search and skip scheme (SAS) was added to minimize the search range. \\ \hline
[17] & 2021 & MCA & Duty cycle & Compared to most basic metaheuristic algorithms, the MCA algorithm has significant advantages in tracking speed and reducing vibration amplitude. \\ \hline
[18] & 2021 & ICS & Duty cycle & By improving the CS algorithm, the problems of high oscillation, high failure rate, and long convergence time in the basic CS algorithm were solved. \\ \hline
[19] & 2022 & Hybrid TSA-PSO & Duty cycle & The effectiveness of TSA-PSO was proved by simulation experiments, and the tracking accuracy reached 97.64\%. Two nonparametric tests were used to prove the reliability of the algorithm. \\ \hline
[20] & 2022 & Hybrid CS-GWO & Duty cycle & By combining GWO and CS in MPPT, the efficiency of the hybrid GWO-CS algorithm was higher than that of the separate GWO and CS. \\ \hline
[21] & 2021 & Hybrid ABC-P\&O & Duty cycle & The ABC-P\&O algorithm has a reasonable computational cost. The experiment proved that ABC-P\&O has a good performance under different PSCs. \\ \hline
[22] & 2022 & MPSO-MP\&O & Duty cycle & This paper combined the improved PSO algorithm with an improved P\&O method by analyzing two sliding mode control structures. The stability of MPP was achieved under changes in physical parameters of different photovoltaic systems. \\ \hline
[23] & 2022 & Hybrid IABC-SHTS & Duty cycle & The combination of an improved Artificial Bee Colony algorithm and multiple heat transfer search algorithms effectively reduced the subjectivity of manually setting parameters in the algorithm, ensuring the universality and accuracy of the search process. \\ \hline \end{tabular}
\end{table} 
Table 1: Research summary of published MPPT algorithms. agents were initialized by equally dividing them to increase the probability of the initial position being near the MPP. In the later stage, a variable step formula was added to improve the convergence speed of the algorithm. Combining the improved ITSO algorithm with INC can effectively overcome the problem of conventional MPPT algorithms falling into LMPP and further accelerate the convergence speed of the algorithm. The ITSO-INC algorithm runs in two stages. The first stage uses the ITSO algorithm because the improved TSO algorithm has a large search range and the ability to jump out of LMPP. The algorithm transitions to the next stage when the number of iterations meets the specified conditions. This second stage uses the INC algorithm for accurate tracking. ITSO is used over a large search range in the early stage, and INC is used for small step tracking over a small range. This two-stage approach can effectively prevent the algorithm from falling into sustained oscillations. The proposed algorithm improves tracking accuracy and search efficiency in the later stage, ensuring that the algorithm can effectively track changes in irradiance by setting algorithm restart conditions. When the irradiance of the photovoltaic array changes significantly, the algorithm is restarted.