# Fine-Tuning of Pre-Trained Deep Face Sketch Models Using Smart Switching Slime Mold Algorithm
## 4 Experimental Results and Analysis
### Evaluation on Large-Scale Benchmark Problems (CEC'2010)
#### 4.1.5 Execution Time Analysis
 & **SMA** & **ESMA** & **LSMA** & **AOSMA** & **AOA** \\ \hline \multirow{8}{*}{F16} & Best & 404.6271 & 410.8071 & 411.5858 & 408.5032 & 409.4019 & 417.1974 \\ \cline{2-8}  & Median & 408.1271 & 413.8228 & 413.8984 & 409.986 & 413.1222 & 417.7382 \\ \cline{2-8}  & Worst & 410.3292 & 416.0813 & 415.0011 & 413.5427 & 414.788 & 418.3869 \\ \cline{2-8}  & Mean & **407.8513** & 413.7724 & 413.679 & 410.2303 & 412.7576 & 417.7455 \\ \cline{2-8}  & Std & 1.285974 & 1.20194 & 0.926444 & 1.286516 & 1.445627 & 0.267426 \\ \hline \multirow{8}{*}{F17} & Best & 2,466,681 & 3,951,118 & 3,821,661 & 4,715,783 & 4,548,857 & 33,001,378 \\ \cline{2-8}  & Median & 3,731,849 & 4,476,318 & 4,312,100 & 5,214,502 & 5,499,177 & 43,564,464 \\ \cline{2-8}  & Worst & 4,662,725 & 5,232,786 & 4,966,740 & 5,797,767 & 6,587,557 & 51,734,401 \\ \cline{2-8}  & Mean & **3,533,019** & 4,512,265 & 4,311,666 & 5,217,711 & 5,541,469 & 43,619,099 \\ \cline{2-8}  & Std & 541,249.5 & 291,273.4 & 276,255 & 290,986.3 & 545,820.7 & 5,020,599 \\ \hline \multirow{8}{*}{F18} & Best & 2.95 \(\times\) 10\({}^{10}\) & 9.7 \(\times\) 10\({}^{10}\) & 5.49 \(\times\) 10\({}^{10}\) & 1.69 \(\times\) 10\({}^{11}\) & 2.63 \(\times\) 10\({}^{11}\) & 1.44 \(\times\) 10\({}^{12}\) \\ \cline{2-8}  & Median & 3.62 \(\times\) 10\({}^{10}\) & 1.17 \(\times\) 10\({}^{11}\) & 7.61 \(\times\) 10\({}^{10}\) & 2.05 \(\times\) 10\({}^{11}\) & 3.07 \(\times\) 10\({}^{11}\) & 1.46 \(\times\) 10\({}^{12}\) \\ \cline{2-8}  & Worst & 4.19 \(\times\) 10\({}^{10}\) & 1.4 \(\times\) 10\({}^{11}\) & 8.61 \(\times\) 10\({}^{10}\) & 2.43 \(\times\) 10\({}^{11}\) & 3.52 \(\times\) 10\({}^{11}\) & 1.47 \(\times\) 10\({}^{12}\) \\ \cline{2-8}  & Mean & **3.63 \(\times\) 10\({}^{10}\)** & 1.17 \(\times\) 10\({}^{11}\) & 7.5 \(\times\) 10\({}^{10}\) & 2.07 \(\times\) 10\({}^{11}\) & 3.07 \(\times\) 10\({}^{11}\) & 1.46 \(\times\) 10\({}^{12}\) \\ \cline{2-8}  & Std & 3.28 \(\times\) 10\({}^{9}\) & 1.09 \(\times\) 10\({}^{10}\) & 7.55 \(\times\) 10\({}^{9}\) & 1.83 \(\times\) 10\({}^{10}\) & 2.33 \(\times\) 10\({}^{10}\) & 6.02 \(\times\) 10\({}^{9}\) \\ \hline \multirow{8}{*}{F19} & Best & 9,885,192 & 11,537,452 & 11,829,648 & 11,496,363 & 13,366,719 & 45,969,764 \\ \cline{2-8}  & Median & 11,948,149 & 13,796,444 & 13,120,993 & 15,140,891 & 19,832,030 & 75,940,439 \\ \cline{2-8}  & Worst & 16,032,746 & 16,494,867 & 17,101,180 & 17,142,757 & 24,729,783 & 1.15 \(\times\) 10\({}^{8}\) \\ \cline{2-8}  & Mean & **12,263,206** & 13,961,159 & 13,262,631 & 14,884,574 & 19,471,422 & 75,902,843 \\ \cline{2-8}  & Std & 1,588,291 & 1,332,594 & 1,048,934 & 1,322,939 & 2,358,851 & 18,375,810 \\ \hline \multirow{8}{*}{F20} & Best & 3.69 \(\times\) 10\({}^{10}\) & 1.2 \(\times\) 10\({}^{11}\) & 7.77 \(\times\) 10\({}^{10}\) & 2.29 \(\times\) 10\({}^{11}\) & 3.29 \(\times\) 10\({}^{11}\) & 1.62 \(\times\) 10\({}^{12}\) \\ \cline{2-8}  & Median & 4.58 \(\times\) 10\({}^{10}\) & 1.47 \(\times\) 10\({}^{11}\) & 9.26 \(\times\) 10\({}^{10}\) & 2.59 \(\times\) 10\({}^{11}\) & 3.8 \(\times\) 10\({}^{11}\) & 1.64 \(\times\) 10\({}^{12}\) \\ \cline{2-8}  & Worst & 6.45 \(\times\) 10\({}^{10}\) & 1.84 \(\times\) 10\({}^{11}\) & 1.08 \(\times\) 10\({}^{11}\) & 2.95 \(\times\) 10\({}^{11}\) & 4.3 \(\times\) 10\({}^{11}\) & 1.65 \(\times\) 10\({}^{12}\) \\ \cline{2-8}  & Mean & **4.76 \(\times\) 10\({}^{10}\)** & 1.48 \(\times\) 10\({}^{11}\) & 9.31 \(\times\) 10\({}^{10}\) & 2.62 \(\times\) 10\({}^{11}\) & 3.81 \(\times\) 10\({}^{11}\) & 1.64 \(\times\) 10\({}^{12}\) \\ \cline{2-8}  & Std & 6.68 \(\times\) 10\({}^{9}\) & 1.42 \(\times\) 10\({}^{10}\) & 8.43 \(\times\) 10\({}^{9}\) & 1.61 \(\times\) 10\({}^{10}\) & 2.59 formed to determine the overhead related to the embedded rules and operations, shown in Table 7. The average computation time for 30 iterations of the F1 function is provided in Table 7. Clearly, S\({}^{2}\)SMA required more time due to the calculations of population status and agent location required to execute the embedded rules and operations.