Hybrid Gradient Descent Grey Wolf Optimizer for Optimal Feature Selection

Pseudocode 2

HGDGWO.

1. Begin

2. Randomly initialize all wolves within the function’s limits

3. Evaluate fitness values of all wolves and sort in ascending order

 a. Set the alpha wolf as the highest fitness value

 b. Set the beta wolf as the second highest fitness value

 c. Set the delta wolf as the third highest fitness value

4. While the maximum number of iterations is not exceeded

 a. For each wolf

  i. Evaluate and using equations (3) and (4)

  ii. Evaluate all 3 values of using equations ((6a), (6b), (6c))

  iii. Evaluate , , and using equations ((7a), (7b), (7c))

  iv. Evaluate the new positions using equation (8)

 b. End

 d. Evaluate the partial derivative of the alpha, beta, and delta wolves from equation (10)

 e. Update the bottom 3 fitness value wolves using equation (9) with the update locations as the alpha, beta, and delta wolves.

 c. Evaluate the fitness values of the wolves

 d. Update the alpha, beta, and delta wolves

5. End

6.