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. 2022 Jan 21;22(3):807. doi: 10.3390/s22030807
Algorithm 1. Reformed Features Optimization Algorithm
Input:g the pack’s total number of grey wolves,
Gth the number of optimization iterations.
Output: lα—Binary position of the grey wolf that is optimal,
f(lα)—Best fitness value
Begin
  •   1.

    Create a population of g wolves with random positions ∈[0,1]

  •   2.

    Find a, b, c solutions that are based on fitness.

  •   3.

    While Criteria for stopping not met do

    For eachwolfj∈packdo

    Calculate l1,l2,l3 using Equations (13), (18) and (22).

    ljh+1 ← crossover among l1,l2,l3 using Equation (26).

    end

    •  I

      Update c, X, S.

    •  II

      Examine the individual wolf positions.

    •  III

      Update a, b, c.

End