## 4 Results and Discussion
### Accuracy Comparison of MTL-AMWO, MTL-WO and M-FNAS  
To verify the accuracy of MTL-AMWO, comparison experiments among MTL-AMWO, MTL-WO, and M-FNAS are carried out. In the experiment, 60% of the dataset is selected as the training dataset, 20% as the validation dataset, and the remaining 20% as the test dataset. The five-fold cross-validation method is carried out 20 times to avoid overfitting. The comparison experimental results of MTL-AMWO, MTL-WO, and M-FNAS for all, quinaries, senarios, and septenaries are shown in Figure 8.  
As shown in Figure 8, the experimental results of all quinaries, senarios, and septenaries among MTL-AMWO, MTL-WO, and M-FNAS are shown in (a), (b), (c), and (d), respectively. In Figure 8, (a) represents all HEA samples of the six datasets. (b) represents the quinary HEA samples in the six datasets. (c) represents the senary HEA samples in the six datasets. (d) represents the septenary HEA samples in the six datasets. The red line with upward triangles shows the accuracy of MTL-AMWO. The blue line with circles represents the accuracy of MTL-WO. The black line with squares shows the accuracy of M-FNAS. Figure 7: Convergence curves of MTL-AMWO, MTL-WO, and M-FNAS in D1, D2, D3, D4, D5, and D6. In order to facilitate the reader's observation, the results of all quinaries, senarios, and septenaries are shown in Tables 2-5. Column 1 is the ID of different datasets. Columns 2 to 4 are the experimental results of M-FNAS, MTL-WO, and MTL-AMWO, respectively. The bold part of the tables is the algorithm with the highest accuracy on the corresponding dataset. \begin{table}
\begin{tabular}{c c c c} \hline
**ID** & **M-FNAS** & **MTL-WO** & **MTL-AMWO** \\ \hline D\({}_{1}\) & 0.845 \(\pm\) 0.023 & 0.896 \(\pm\) 0.032 & **0.922 \(\pm\) 0.063** \\ D\({}_{2}\) & 0.848 \(\pm\) 0.037 & 0.879 \(\pm\) 0.029 & **0.909 \(\pm\) 0.035** \\ D\({}_{3}\) & 0.806 \(\pm\) 0.030 & 0.889 \(\pm\) 0.054 & **0.917 \(\pm\) 0.060** \\ D\({}_{4}\) & 0.769 \(\pm\) 0.046 & 0.807 \(\pm\) 0.076 & **0.883 \(\pm\) 0.038** \\ D\({}_{5}\) & 0.696 \(\pm\) 0.036 & 0.783 \(\pm\) 0.043 & **0.826 \(\pm\) 0.086** \\ D\({}_{6}\) & 0.878 \(\pm\) 0.041 & 0.902 \(\pm\) 0.024 & **0.951 \(\pm\) 0.049** \\ \hline \end{tabular}
\end{table} 
Table 2: Experiment results of M-FNAS, MTL-WO, and MTL-AMWO in all. Figure 8: The accuracy comparison of MTL-AMWO, MTL-WO, and M-FNAS. (**a**) All, (**b**) Quinaries, (**c**) Senaries, and (**d**) Septenaries. As shown in Table 2, the accuracy of MTL-AMWO is the highest in D\({}_{1}\), D\({}_{2}\), D\({}_{3}\), D\({}_{4}\), D\({}_{5}\), and D\({}_{6}\); the accuracies are 0.922, 0.909, 0.917, 0.883, 0.826, and 0.951, respectively. The accuracy of MTL-WO is the second highest in D\({}_{1}\), D\({}_{2}\), D\({}_{3}\), D\({}_{4}\), D\({}_{5}\), and D\({}_{6}\); the accuracies are 0.896, 0.879, 0.889, 0.807, 0.783, and 0.902, respectively. As shown in Table 3, the accuracy of MTL-AMWO is the highest in D\({}_{1}\), D\({}_{2}\), D\({}_{3}\), D\({}_{4}\), D\({}_{5}\), and D\({}_{6}\); the accuracies are 0.857, 0.833, 0.917, 0.875, 0.800, and 0.867, respectively. The accuracy of MTL-WO is the second highest in D\({}_{1}\), D\({}_{2}\), D\({}_{3}\), and D\({}_{6}\); the accuracies are 0.786, 0.750, 0.769, and 0.733, respectively. As shown in Table 4, the accuracy of MTL-AMWO is the highest in D\({}_{1}\), D\({}_{2}\), D\({}_{3}\), D\({}_{4}\), D\({}_{5}\), and D\({}_{6}\); the accuracies are 0.932, 0.916, 0.923, 0.909, 0.900, and 0.941, respectively. The accuracy of MTL-WO is the second highest in D\({}_{1}\), D\({}_{2}\), D\({}_{3}\), D\({}_{4}\), D\({}_{5}\), and D\({}_{6}\); the accuracies are 0.866, 0.833, 0.846, 0.818, 0.800, and 0.884, respectively. As shown in Table 5, the accuracy of MTL-AMWO is the highest in D\({}_{1}\), D\({}_{2}\), D\({}_{3}\), D\({}_{4}\), D\({}_{5}\), and D\({}_{6}\); the accuracies are 0.846, 0.889, 0.909, 0.875, 0.900, and 0.900, respectively. The accuracy of MTL-WO is the second highest in D\({}_{1}\), D\({}_{2}\), D\({}_{3}\), D\({}_{4}\), and D\({}_{5}\).