Table 8.
The impact of features descriptors on the performance of AOA against other recent optimizers over Precision measures
| Precision | GT dataset | FEI dataset | ||||
|---|---|---|---|---|---|---|
| Algorithms | HOG | LBP | GLCM | HOG | LBP | GLCM |
| HHO | 0.9014 | 0.8677 | 0.8949 | 0.9903 | 0.9347 | 0.9951 |
| SCA | 0.8891 | 0.8400 | 0.8870 | 0.9880 | 0.9560 | 0.9877 |
| EO | 0.9018 | 0.8414 | 0.8927 | 0.9928 | 0.9642 | 0.9904 |
| EPO | 0.8960 | 0.8475 | 0.8883 | 0.9882 | 0.9383 | 0.9881 |
| MRFO | 0.9052 | 0.8352 | 0.8913 | 0.9951 | 0.9510 | 0.9929 |
| HGSO | 0.8908 | 0.8361 | 0.8874 | 0.9928 | 0.9417 | 0.9927 |
| MVO | 0.8974 | 0.8399 | 0.8977 | 0.9927 | 0.9478 | 0.9927 |
| AOA | 0.9099 | 0.8529 | 0.8980 | 0.9951 | 0.9457 | 0.9951 |