## 4 Discussion
This scenario is peculiar with the application of the SR method regarding a low-velocity layer (e.g., cavity) surrounded by a high-velocity media [48]. In the CGO method, using the same size of the synthetic model from the LOM, we observed that obtaining a good inversion result for the SR model using a computer with 12 logical processors configuration (6 cores) takes a longer time than the DCR method. This is because the DCR method produces electrical perturbation and estimates the apparent resistivity of the model at once; however, the SR method first computes the travel time from one source to all receivers sequentially and thereafter repeats the same procedure for all other available sources. To optimize the computation time of both SOO and MOO, we made the misfit algorithm part of the code to run on parallel computing by using the built-in parallel computing (e.g., parfor loop) command in MATLAB. This process enhanced the computation cost of the hybrid global optimization algorithm. For instance, it took 189.75 min to obtain the same result as in Figure 8b (single-objective GA for the positive anomaly DCR model) without parallel computing, whereas it took 55.25 min (71% run time optimization) to obtain the same output with parallel computing. Similarly, running the single objective genetic algorithm for the positive anomaly velocity (SR) model for 100 generations took 664.56 min without parallel computing, whereas it took 75.66 min (89% run time optimization) to obtain the same result with parallel computing. The percentage of run time optimization depends on the population size, number of generations, and type of geophysical method involved. To make the synthetic test challenging for the CGO algorithm, we added 3% Gaussian noise to the data resulting from the positive anomaly model. Although the output did not match noise-free data perfectly, a larger portion of the anomaly was recovered (Figures 15 and 16). Regarding the GA population size, we observed that the CGO algorithm involving the DCR and SR methods performed better with an increasing number of populations. For example, Figure 15 shows a graduate improvement in the model resolution as the number of populations increased in the DCR genetic algorithm result. Similarly, the CGO algorithm offered a better performance with increasing generations. Notwithstanding, increasing the number of populations and generations prolonged the computation run time (Figure 17). Considering these criteria (number of generations, population size, and computation time), we applied an average population size of 500 and 500 generations for both DCR and SR combined global optimization inversions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Inversion Type** & **Cores** & **Population Size** & **Misfit (\%)** & **Generations** & **Run Time (min)** \\ \hline DCR & Dyke & 16 & 9500 & 1.04 & 2500 & 6642.15 \\ SR & Dyke & 16 & 4800 & 3.32 & 200 & 6803.45 \\ DCR & Positive & 6 & 9500 & 1.40 & 2500 & 13,962.00 \\ DCR & Negative & 6 & 9500 & 2.70 & 2500 & 11,426.68 \\ DCR & Pos. \& Neg. & 4 & 9500 & 14.51 & 2500 & 13,929.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of the convectional global optimization inversion results parameter for both DCR and SR (dyke, positive, positive and negative anomalies model) methods. Figure 16: Test of the combined optimization algorithm using data (both DCR and SR) with added 3% gaussian noise; (**a**) synthetic dc-resistivity model, (**b**) multi-objectives inverted resistivity model (noisy), (**c**) multi-objectives inverted resistivity model (noiseless), (**d**) synthetic velocity model, (**e**) multi-objectives inverted velocity model (noisy), and (**f**) multi-objectives inverted velocity model (noiseless). The dark red lines are used to mark the anomaly boundary.