Table 2 Parameter Settings for Various Optimization Algorithms.
Common Parameters for All Algorithms | |
|---|---|
Parameter | Setting/Value |
Population Size (N) | 30 (for all algorithms) |
Maximum Iterations (MaxIter) | 500 |
Algorithm-Specific Parameters | |
Young’s Double-Slit Experiment (YDSE) | \(\bullet\) Scaling Factor (F): 0 - 1 \(\bullet\) Population Size: Problem-specific \(\bullet\) Iterations: Predefined, depending on problem complexity |
Differential Evolution (DE) | \(\bullet\) Mutation Factor (F): 0.5 - 1 \(\bullet\) Crossover Rate (CR): 0.7 - 0.9 \(\bullet\) Population Size: 50 - 100 |
Ant Lion Optimizer (ALO) | \(\bullet\) Random Walk Parameters: Fixed or random step size for ant’s walk \(\bullet\) Exploitation Mechanism: Ant lion traps and random walks \(\bullet\) Selection Mechanism: Roulette wheel selection |
Sooty Tern Optimization Algorithm (STOA) | \(\bullet\) Exploitation Mechanism: Guided by the best sooty tern and nearest predator \(\bullet\) Selection Mechanism: Determined by distance to the best sooty tern |
Rat Swarm Optimizer (RSO) | \(\bullet\) Swarm Size: 30 - 100 \(\bullet\) Behavioral Parameters: Control exploration and exploitation \(\bullet\) Selection Mechanism: Based on fitness and proximity |
