## 3 Conventional Cat Swarm Optimization  
Cat swarm optimization (CSO) is a global optimization algorithm based on cat behavior that was first proposed by [PERSON] et al. in 2006 [21]. CSO mimics two cat behaviors,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Maximum output power (\(P_{max}\)) & 20 W \\ Current of maximum output power (\(I_{mpp}\)) & 1.1 A \\ Voltage of maximum output power (\(V_{mpp}\)) & 18.18 V \\ Short-circuit current (\(I_{sc}\)) & 1.15 A \\ Open-circuit voltage (\(V_{\alpha\alpha}\)) & 22.32 V \\ Overall dimensions of single module & \(395\times 345\times 17\) mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of electrical performance parameters for SWM20W photovoltaic modules made by MPPTSun [22].  
Figure 1: I-V and P-V characteristic curves derived from module simulation at 25 \({}^{\circ}\)C and different irradiance levels.  
seeking and tracking, to solve optimization problems; thus, it includes a seeking mode and a tracking mode. The seeking mode simulates the state of a cat relaxing and looking around, while the tracking mode simulates a cat tracking a target. A flowchart of conventional CSO is shown in Figure 2.  
Figure 2: Flowchart of conventional CSO.  
First, the method requires initialization and the setting of certain parameters, including the quantity of cats, the maximum number of iterations, and the initial location, speed, and flag of each cat. The parameters are described as follows:  
* Cat quantity: This ensures the number of cats needed for the algorithm, which affects the converging capability and speed of CSO. In this study, we used 6 cats.
* Initial location (duty cycle of converter): The initial location of each cat should be set right from the start; in this study, they were set randomly.
* Speed (tracking pace): After setting the initial location, the initial speed can be calculated accordingly.
* Cat flag: This parameter refers to the Boolean value indicated with only \"yes\" or \"no\". \"Yes\" means that the cat is in seeking mode and \"no\" means that the cat is in tracking mode.
* Maximum number of iterations: The condition for iteration termination was set here to ensure that the CSO operation would cease within a certain time frame.  
In step 1, the current fitness value (i.e., currently tracked power value) for each cat was allocated at the current location and the fitness value was assessed, and the location (\(\tau_{best}\)) of the cat with the optimal fitness value was saved. In CSO, the cats are assigned to different modes according to the set mixture ratio (MR). The MR represents the proportion of cats distributed to tracking mode, and has a value between 0 and 1. In the program, the MR was often set with smaller numbers. For example, if 10 cats were utilized in CSO with an MR of 0.2, 2 cats would be assigned to tracking mode and 8 cats would be assigned to seeking mode. The flag for cats assigned to seeking mode would be set as \"yes\", and the main task of seeking mode is to update the cat location. The flowchart of CSO in Figure 2 describes seeking and tracking modes on the left and right sides, respectively.  
Once the cats were assigned to the two work modes, each cat performed the task of seeking or tracking in the current location with the optimal fitness value. Subsequently, the fitness value of the individual allocated location was compared with the current optimal fitness value. If the two values were unequal, the program continued to seek and track, so better fitness values could be allocated. However, if the two values were equal, the program would be checked for compliance with the terminating condition: the maximum iteration number. If the maximum iteration number was reached, the program ceased to operate. Conversely, if the maximum iteration number was not reached, the program continued to operate. Since the iteration number set in this study was relatively small at 30, the program did not cause slower tracking speed due to excessive iterations.