An Innovative Excited-ACS-IDGWO Algorithm for Optimal Biomedical Data Feature Selection

Algorithm 2: Pseudocode for the GWO.

1 Begin:

2 Initialize population size , parameter , coefficient vectors , and maximum number of iterations

3 Set {Counter initialization}

4 for ( =1: ) do

  Randomly generate an initial population

5  Evaluate the fitness function of each agent (solution) i.e.

6 end for

7 Assign the values of the 1^st,2^nd and 3^rd best solutions i.e. , and , respectively

8 repeat

9 for ( =1: ) do

10 Update each search agent in the population using Equation (11)

11 Decrease the value of using Equation (14)

12 Update the coefficients and as shown in Equation (9) and Equation (10), respectively

13 Evaluate the fitness function of each search agent (vector)

14 end for

15 Update the vectors , and

16 Set {Iteration counter increasing}

17 Until () {termination criteria satisfied}

18 Report the best solution

Algorithm 2: Pseudocode for the GWO.