## 5 Conclusions  
In this study, we developed a hybrid power converter model based on a unified EA. Three power converter variables were optimized simultaneously to increase the efficiency of the model. Furthermore, we proposed a unified EA that integrated the properties of DE and genetic GA to evaluate the modified hybrid power converter. This study employed the unified EA to optimize the zero-ripple duty cycle (\(D_{\text{Z}}\)) in order to minimize the input-current ripple. Based on the simulation results obtained in this study, the proposed method achieved a lower input-current ripple value across the operating range when compared to the baseline method. By analyzing the percentage improvement results that were found, the proposed method improved the majority of cases by nearly 50%. In addition, the proposed algorithm was compared with DE and GA, and the simulation results indicated that the proposed algorithm obtained a low input-current ripple when compared to those with different gain values. Due to this, the efficiency of the proposed method and the hybridization of the current model was proven. Consequently, the proposed algorithm outperformed the baseline technique, DE, and the GA algorithms. In addition, it performed better than the baseline algorithm in terms of speed.  
In the future, we intend to optimize other parameters of the power converter (such as the indicator factor, switching frequency, and output resistance) to enhance the performance. Additionally, we would like to apply the hybrid algorithm to other hybrid power converters.  
Conceptualization, S.G. and S.H.; methodology, S.G. and S.H.; software, S.G.; validation, S.G.; formal analysis, S.G., M.S., J.-J.P., M.K. and Y.J.; investigation, S.G., M.S., J.-J.P. and Y.J.; resources, S.G.; data curation, S.G., J.-J.P. and M.K.; writing--original draft preparation, S.G., M.S. and Y.J.; writing--review and editing, S.G.; visualization, S.G. and M.K.; supervision, S.H.; project administration, S.H.; funding acquisition, S.H. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Gain**} & \multicolumn{4}{c}{**Input-Current Ripple (\(\Delta_{ig}\))**} \\ \cline{2-5}  & **Proposed** & **DE** & **DE*** & **GA** \\ \hline
4.8 & **0.5797** & 0.6535 & 0.5910 & 1.0793 \\ \hline
4.9 & **0.6217** & 0.7050 & 0.6450 & 1.2583 \\ \hline
5 & **0.6652** & 0.6843 & 0.6792 & 1.3074 \\ \hline
5.1 & **0.7102** & 0.7431 & 0.7441 & 1.3449 \\ \hline
5.2 & **0.7848** & 0.7932 & 0.7936 & 1.3159 \\ \hline
5.3 & **0.7166** & 0.7579 & 0.7214 & 1.3645 \\ \hline
5.4 & **0.6714** & 0.8528 & 0.6949 & 1.3718 \\ \hline
5.5 & **0.7472** & 0.7864 & 0.7645 & 1.5165 \\ \hline
5.6 & **0.7614** & 0.9056 & 0.7806 & 1.5898 \\ \hline
5.7 & **0.7751** & 0.7966 & 0.7888 & 1.4792 \\ \hline
5.8 & **0.8227** & 0.8491 & 0.8499 & 1.7313 \\ \hline
5.9 & **0.8362** & 0.9011 & 0.8652 & 1.6052 \\ \hline
6 & **0.8493** & 0.9514 & 0.8783 & 1.4860 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cont.  
**Funding:** This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20202010600010).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable, the study does not report any data.  
**Conflicts of Interest:** The authors declare no conflict of interest.