## 4 Simulation Setup and Results
### Comparison between the Proposed Method and Baseline Algorithm  
The approach proposed in [31] was selected as the baseline algorithm to validate the performance of the proposed algorithm. The control parameters for the baseline algorithm were set as follows: the mutation scaling factor F was selected from a uniform distribution between 0.2 and 0.8; the crossover rate was set as 0.2 and w was maintained as 10, as recommended in [31]. F, CR, and w were set as 0.3, 0.75, and 10, respectively, for the proposed method. The population size and the number of generations were set as 20 and 50, respectively. Because the EA is a stochastic algorithm, these results are based on the average value collected from 30 independent executions. Moreover, Table 1 provides the relevant parameters for the hybrid power converter. The mean values of the input-current ripple obtained over 30 independent runs are presented in Table 2. In addition, we have provided the hybrid power converter parameters \(D\), \(k\), and \(D_{Z}\). It is evident from the results presented in this table that the proposed method achieved a lower input-current ripple value in all the cases. It achieved a significant improvement in the input-current ripple value as \(G\) increased. Table 2 reveals that the optimization of \(D_{Z}\) simultaneously optimized both \(D\) and \(k\). The simulation results of the proposed method are better than those of the baseline algorithm because of the optimization of \(D_{Z}\). The baseline algorithm maintained \(D_{Z}\) at a constant of 0.6. The proposed method performed better for each gain instance at different values of \(D_{Z}\). For example, the proposed method optimized \(D_{Z}\) to 0.7998 for a gain of 3.1 and 0.9519 for a gain of 4. Thus, the optimization of \(D_{Z}\) improved the performance of the model. In the summary of Table 2, the proposed hybrid algorithm (combines DE and GA) method performed better than a baseline (basic DE) model. Therefore, due to hybridization, the proposed method minimizes input-current ripple better than the baseline method, demonstrating its efficiency. In particular, as the G value increases, the efficiency of the proposed method increases significantly because it incorporates the properties of DE and GA. \begin{table}
\begin{tabular}{c c} \hline Input Voltage V\({}_{\text{in}}\) & 20 V \\ \hline Inductor Factor kL & 0.6666 \\ \hline Switching Frequency fS & 50 kHz \\ \hline Output Resistance R & 60 \(\Omega\) \\ \hline L\({}_{1}\) & 66 \(\mu\)H \\ \hline L\({}_{2}\) & 100 \(\mu\)H \\ \hline \end{tabular}
\end{table} 
Table 1: Parameters for simulation setting. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Gain**} & \multirow{2}{*}{**Methodology**} & \multicolumn{2}{c}{**Input-Current**} & \multirow{2}{*}{\(D\)} & \multirow{2}{*}{\(k\)} & \multirow{2}{*}{\(D_{Z}\)} \\  & & & & & \\ \hline \multirow{3}{*}{3} & Proposed & **0.0758** & 0.5807 & 0.6751 & 0.9999 \\ \cline{2-5}  & Baseline & 0.0844 & 0.5806 & 0.6753 & 0.6 \\ \cline{2-5}  & Proposed & **0.0203** & 0.5951 & 0.6688 & 0.7998 \\ \cline{2-5}  & Baseline & 0.0520 & 0.5950 & 0.6689 & 0.6 \\ \hline \multirow{3}{*}{3.166} & Proposed & **1.32 \(\times\) 10\({}^{-5}\)** & 0.6000 & 0.6666 & 0.7888 \\ \cline{2-5}  & Baseline & 0.0105 & 0.6000 & 0.6667 & 0.6 \\ \cline{2-5}  & Proposed & **0.0180** & 0.6045 & 0.6646 & 0.9999 \\ \cline{2-5}  & Baseline & 0.0508 & 0.6037 & 0.6688 & 0.6 \\ \hline \multirow{3}{*}{3.3} & Proposed & **0.0710** & 0.6175 & 0.6588 & 0.7010 \\ \cline{2-5}  & Baseline & 0.1527 & 0.6144 & 0.6744 & 0.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of proposed method with baseline algorithm.