**6. Conclusions**

This paper introduces a method of SRM design optimization by genetic algorithm. Non-dominated sorting multi-objective genetic algorithm (NSGA-II) is used for its high performance and intensification in optimization problems. Since the NSGA-II optimization technique provides optimal set of solutions (non-dominated front), the final decision is left to the designer to choose the most convenient design of optimal non-dominated front to be picked. FEA analysis is adopted in optimization process as it provides high accuracy. Core losses are calculated numerically based on flux density waveforms and hysteresis loops. Three objective functions *Tav* ,*η* and *Wiron* were chosen to be optimized. The results show the variation of variables to optimize these objectives only, regardless of other important considerations like torque ripples, acoustic noise and mechanical vibrations. This proves the success of the optimization program as a framework to optimize the specified objective functions. However, further work is required to include more objective functions and to use this framework for a specific applications.

**Author Contributions:** Funding acquisition, M.I. and H.R.; Methodology, M.E.-N.; Resources, M.E.- N.; Validation, M.I. and H.R.; Writing—original draft, M.A.; Writing—review & editing, M.E.-N. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Data Availability Statement:** Data available on request from the authors.

**Conflicts of Interest:** The authors declare no conflict of interest.
