Table 3.
The impact of features descriptors on the performance of AOA against other recent optimizers over fitness measures
| Fitness | GT dataset | FEI dataset | ||||
|---|---|---|---|---|---|---|
| Algorithms | HOG | LBP | GLCM | HOG | LBP | GLCM |
| HHO | 0.8947 | 0.8644 | 0.8913 | 0.9776 | 0.9262 | 0.9849 |
| SCA | 0.8914 | 0.8653 | 0.8873 | 0.9820 | 0.9518 | 0.9841 |
| EO | 0.9002 | 0.8642 | 0.8927 | 0.9853 | 0.9461 | 0.9829 |
| EPO | 0.8966 | 0.8658 | 0.8887 | 0.9804 | 0.9351 | 0.9780 |
| MRFO | 0.8950 | 0.8593 | 0.8902 | 0.9837 | 0.9412 | 0.9853 |
| HGSO | 0.8902 | 0.8543 | 0.8833 | 0.9837 | 0.9298 | 0.9834 |
| MVO | 0.8981 | 0.8589 | 0.8954 | 0.9805 | 0.9335 | 0.9825 |
| AOA | 0.9015 | 0.8690 | 0.8969 | 0.9882 | 0.9534 | 0.9858 |