### Contribution and Paper Organization
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Algorithm** & **PV Type** & **PV Model** & **Disadvantage** & **Advantage** \\ \hline MPA [26] & KC200 GT and MSX-60 & DDM & \begin{tabular}{c} Convergence at an \\ early stage \\ \end{tabular} & \begin{tabular}{c} A high degree of accuracy \\ in the solution \\ \end{tabular} \\ \cline{3-4}  & & \begin{tabular}{c} Stuck in local minima \\ \end{tabular} & \begin{tabular}{c} Excel exploratory skills \\ \end{tabular} \\ \hline AGA [27] & RTC France Si cell & SDM & \begin{tabular}{c} Caught in the trap of \\ local minima and a lack \\ of local search capability \\ \end{tabular} & \begin{tabular}{c} A reasonable degree of \\ accuracy and identifying \\ promising search areas to \\ find solutions \\ \end{tabular} \\ \hline IEO [28] & RTC France Si cell, PhotoWatt-PWP201, ST40, and SM55 & SDM, DDM & Long computation times & \begin{tabular}{c} High level of accuracy \\ A good ability to explore \\ and exploit \\ \end{tabular} \\ \hline SMA [29] & RTC France Si cell and PhotoWatt-PWP201 & SDM, DDM & \begin{tabular}{c} It takes a long time \\ to compute \\ \end{tabular} & \begin{tabular}{c} A high degree of accuracy \\ and a good ability to \\ explore and exploit \\ \end{tabular} \\ \hline OAHHO [30] & RTC France Si cell, PhotoWatt-PWP201, PVM & SDM, DDM & Not specified & \begin{tabular}{c} Rapid convergence rates \\ \end{tabular} \\ \cline{3-4}  & \
\begin{tabular}{c} Avoiding local optimum \\ situations \\ \end{tabular} \\ \cline{3-4}  &
\begin{tabular}{c} High-quality solutions \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._factor in the attack phase is adjusted based on the prey's position, and a random value controls the turning factor. It is believed that the proposed attack operator will improve the behavior of the algorithm in the global search as well as its convergence speed. When it comes to estimating the optimal parameters for PV cells and models, the CO and proposed ICO are compared to two recent well-established algorithms for parameter extraction of PV models (i.e., PGJAYA [6] and SEDE [14]) and eight well-known original algorithms: DE [33], PSO [34], GA [35], TLBO [36], JAYA [37], SSA [38], WSO [13], and GWO [39]. This paper contributes the following:  
* A simplified and improved version of the CO is introduced for parameter estimation of PV models. * The search phase is controlled according to the leader's position, and its step length is adjusted following the sorted population. Hence, the proposed search strategy facilitates both global and local search capabilities. * In the attack phase, the interaction factor is adjusted based on the prey's position, while the turning factor is determined based on a random value. Using the proposed attack operator, the algorithm is expected to perform better during global searches and achieve faster convergence. * An extensive study is conducted and compared with other well-established meta-heuristic algorithms to evaluate the ICO's effectiveness. The following is a summary of an overview of the remainder of the paper. In Section 2, we describe in detail the SDM, DDM, and PVMM. The proposed ICO algorithm is presented in Section 3. A simulation and evaluation of the results of the experiment are presented in Section 4. Finally, Section 5 makes some closing remarks.