Research Article
Bearing Fault Diagnosis Using a Support Vector Machine Optimized by an Improved Ant Lion Optimizer
Table 2
Details of different algorithm parameters.
| Algorithm | Searching range | Convergence condition | Parameter | Initial value |
| EALO |
| | Ant population | 50 | Ant lion population | 25 | Escape ant population | 10 | Ant escape probability | 0.7 |
| ALO |
| | Ant population | 50 | Ant lion population | 25 |
| GA-1 |
| || | Population | 50 | Crossover probability | 0.1 | Mutation probability | 0.01 |
| GA-2 |
| || | Population | 50 | Crossover probability | 0.4 | Mutation probability | 0.01 |
| GA-3 |
| || | Population | 50 | Crossover probability | 0.1 | Mutation probability | 0.1 |
| GA-4 |
| || | Population | 50 | Crossover probability | 0.4 | Mutation probability | 0.1 |
| GA-5 |
| || | Population | 50 | Crossover probability | 0.7 | Mutation probability | 0.5 |
| PSO-1 |
| || | Particle size | 50 | Acceleration factor C1 | 0.1 | Acceleration factor C2 | 0.1 | Weight W | 0.5 |
| PSO-2 |
| || | Particle size | 50 | Acceleration factor C1 | 0.5 | Acceleration factor C2 | 0.1 | Weight W | 0.8 |
| PSO-3 |
| || | Particle size | 50 | 1.0 | Acceleration factor C1 | 0.5 | Acceleration factor C2 | 1.0 | Weight W | 100 |
| PSO-4 |
| || | Particle size | 50 | Acceleration factor C1 | 1.5 | Acceleration factor C2 | 1.7 | Weight W | 1.0 |
| PSO-5 |
| || | Particle size | 50 | Acceleration factor C1 | 2.0 | Acceleration factor C2 | 2.0 | Weight W | 1.5 |
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