## 7 Conclusions  
The study is devoted to the comparative analysis of various computer-aided iterative identification methods based on optimization algorithms such as the differential evolution (DE) and the Nelder-Mead (NM) in LSPMSM lumped-parameter model identification. The comparison was carried out on the example of transients calculated for the LSPMSM model with given parameters. Comparing the DE and NM algorithms at the same initial approximation, it can be concluded that the NM algorithm provided the final value of the objective function, proportional to the deviation of the identified parameters, 345 times fewer than DE. In addition, NM demanded the number of iterations to be two times fewer than DE. Comparing the DE and NM algorithms with a random initial approximation, it can be concluded that the NM provided the final value of the objective function 4.5\(\cdot\)10\({}^{10}\) times fewer than DE. It can be concluded that, even though NM is an unrestricted local search method, it is able to successfully solve the problem under consideration, being more computationally efficient and accurate than DE. In this case, the DE method can be used at the initial stage of identification to find a suitable initial approximation for the NM. For a more accurate identification of the parameters, when a suitable initial approximation has already been found, the NM should be used. In addition, in this study, it is proposed to stop and restart the considered identification procedures with a change in the algorithm parameters after a certain number of iterations and use the previously obtained results as a new initial approximation. Such multistage optimization (three stages have been found to be enough) leads to an increase in the computational speed and accuracy for both DE and NM. In this case, the NM algorithm provided the final value of the objective function 237 times fewer and required the number of iterations to be three times fewer than the DE with the same initial approximation. With a random initial approximation, in this case, the NM provided the final value of the objective function four times fewer than DE. Although the article demonstrates the validity of the presented results only in the case of identifying the parameters of the LSPMSM model based on the results of simulated transients, in the future, the results will be used, and the conclusions will be verified when solving the problem of parametric identification of a real LSPMSM based on real transients. Figure 9: The best objective function values vs. iterations for the random initial approximations. **Author Contributions:** Conceptual approach, A.P., V.K. and V.P. ; data curation, A.P. and S.O. ; software, A.P., S.O. and V.K. ; calculations and modelling, A.P., S.O., V.D., V.K. and V.P. ; writing--original draft, A.P., S.O., V.D., V.K., V.G. and V.P. ; visualization, A.P. and V.K. ; review and editing, A.P., S.O., V.D., V.K., V.G. and V.P. All authors have read and agreed to the published version of the manuscript. **Funding:** The work was partially supported by the Ministry of Science and Higher Education of the Russian Federation (through the basic part of the government mandate, Project No. FEUZ-2023-0013). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data are contained within the article. **Acknowledgments:** The authors thank the editors and reviewers for their careful reading and constructive comments. **Conflicts of Interest:** The authors declare no conflict of interest.