## 5 Conclusions  
In this research, two novel meta-heuristic algorithms, including osprey optimization and the walrus optimization algorithm, are successfully applied to optimize the size and locations of wind turbines in the IEEE-85 bus distribution power system for power loss minimization. The comparison of the results obtained by the two algorithms indicates that the WOA completely outperforms the OOA, especially in terms of reaching the best power loss value in the first three cases of the research. Besides always reaching the better values of active power loss (Lst. Loss) in those cases, the WOA is also more effective than COA in other criteria, such as Aver. Loss, Hst. Loss, and STD. Remarkably, the better percentages of WOA over COA in those criteria of Case 1 are 1.28%, 5.12%, and 87.20%; the percentages of Case 2 are 20.14%, 25.25%, and 65.73%. Lastly, in Case 3, the WOA is better than COA by 27.23% for Aver. Loss. Regrading Hst. Loss and STD, the results of WOA are not better than those of COA. Moreover, the WOA is also reapplied to reach the optimal power loss value in the case that both load demand variation and dynamic wind power supplied within 24 h are all evaluated. The results obtained by the WOA, in this case, indicate that for reaching the best power loss value, both supplied energy and power factors of WTs must be simultaneously optimized; otherwise, the use of maximum supplied energy with the optimized power factors of WTs will not return the positive results. Hence, WOA proves to be a highly effective search method to deal with the given problem, even in the scaled-up version of the initial problem. However, the research also has several downsides that need to be improved for better quality in the future:  
1. Other objective functions, such as minimizing the voltage deviation index (VDI), minimizing capital costs, etc., are not considered. 2. Solar energy and the combination of both solar energy and wind power are not evaluated in this research. 3. The whole research is still conducted on the IEEE-85 bus distribution network, not the real one in Vietnam. 4. The availability of wind power in terms of installed site, short-circuit ratings of existing switchgear, circuit capacity, the feasibility of connection, and environmental effects on buses still need to be thoroughly investigated. 5. The economic exchange for the case that power supplied from WTs is larger than load demand and flow back to the transmission network is not discussed. By fully understanding these drawbacks, future research must resolve all problems mentioned above for better quality overall. Conceptualization: T.T.N. ; methodology: M.Q.D. ; data acquisition: T.M.P. ; coding and simulations: M.P.D. ; writing--original draft preparation: T.M.P. ; writing--review and editing: A.T.D. ; formal analysis: M.Q.D. ; funding acquisition: M.Q.D. ; resources: T.M.P. and A.T.D. ; supervision: M.Q.D. All authors have read and agreed to the published version of the manuscript. **Funding:** Funds for Science and Technology Development of the University of Danang under project number B2023-DN01-03. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data are reported in Appendix A. **Acknowledgments:** This research is funded by Funds for Science and Technology Development of the University of Danang under project number B2023-DN01-03. **Conflicts of Interest:** The authors have no relevant financial or non-financial interests to disclose. The authors have no conflicts of interest to declare that are relevant to the content of this article. **Nomenclature**  
\(Fit_{W_{E}}\) The fitness value of the selected walrus. \(Fit_{W_{n}}\) The fitness value of the walrus under consideration. \(I_{DLi}^{Hst}\) The rated current value of the \(i^{th}\) distribution line. \(I_{WT,i}\) The current value of the \(i^{th}\) distribution line after installing wind turbines. \(LB_{n}\) and \(UB_{n}\) The lowest and highest limits of the search space.