**6. Conclusions**

For MT inversions, we propose a memetic strategy on the basis of traditional PSO, which includes four parts: opposition-based learning, dynamic inertia weights, sine-cosine acceleration coefficients and gene mutation. The test results of the different models show that reverse learning can selectively enhance the population diversity and accelerate the optimization process. The dynamic inertia weights based on sine mapping can strengthen the optimization ability by fusing previous cognitive experience. By balancing the influence of individual cognition and group cognition on the evolutionary process, the sine-cosine acceleration coefficients can improve the global optimization capability in the early stages of the optimization process and maintain convergence stability in the later stages. Genetic mutation can further strengthen the ability to find the best solution by enhancing the population diversity.

The noise test verifies that this memetic strategy can improve the noise immunity of PSO. Moreover, the proposed strategy outperforms the traditional PSO method on the measured MT data. We have greatly improved the ability of PSO to invert MT data by enhancing the diversity of the population and fusing the individual and social cognition of the population.

**Author Contributions:** Conceptualization, R.L. and L.G.; methodology, L.G.; software, N.Y.; validation, L.G., R.L. and E.W.; formal analysis, E.W. and J.L.; data curation, X.F.; writing—original draft preparation, R.L.; writing—review and editing, Y.L. and J.L.; funding acquisition, N.Y. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was jointly supported in part by the National Natural Science Foundation of China (42074081 and 41974158), the Postdoctoral Science Foundation of Chongqing under Grant (cstc2019jcyj-bshX0105 and cstc2020jcyj-bshX0115), and the Fund from the Key Laboratory of Geophysical Electromagnetic Probing Technologies of Ministry of Natural Resources (KLGEPT202002).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

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