*3.6. Fitness*

The fitness in the optimization process refers to the objective function setting in MT inversions. The L2 norm is used to define the misfit between the observed MT response data and the predicted response data. The fitness can be expressed as:

$$\begin{aligned} fit &= \mathfrak{c}\_{rho} fit\_{rho} + \mathfrak{c}\_{phi} fit\_{phi} \\ &= \mathfrak{c}\_{rho} \left\| 1 - \mathfrak{p}\_{prcd} \left/ \rho\_{obs} \right\| \right\|^2 + \mathfrak{c}\_{phi} \left\| 1 - \mathfrak{p}\_{prcd} \left/ \rho\_{obs} \right\| \right\|^2 \end{aligned} \tag{14}$$

where the overall fitness is composed of apparent resistivity fitness and phase fitness model fitness. Their weight coefficients are *crho* and *cphi*. Since the apparent resistivity and phase are variables with different units, in order to transform the apparent resistivity and phase fitness into a unified dimension, we normalize the response data and prediction data.

#### **4. Test Model**

We designed two common geoelectric models, a three-layer model and a five-layer model. These models were used to generate synthetic MT response data. Different PSO methods predicted the geoelectric models based on these response data, and the MT responses were obtained through MT forward modeling. Comparing the geoelectric model and the responses predicted by different methods allowed us to test the effect of our memetic strategy.
