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. 2021 Mar 26;177:114920. doi: 10.1016/j.eswa.2021.114920
  • 1:

    Input the parameters of GGWO

  • 2:

    for i = 1:npop # create the initial population

  • 3:

     Create a random solution

  • 4:

     Calculate the fitness

  • 5:

    end for

  • 6:

    Sort the solutions based on the fitness values

  • 7:

    Set the best three solutions as Alpha, Beta, and Delta, respectively.

  • 8:

    Set the remaining wolves as Omegas

  • 9:

    it = 1

  • 10:

    while stopping criterion is not met # main loop of the algorithm

  • 11:

     for i = 1: npop

  • 12:

      Calculate and update A and C

  • 13:

      Calculate the value of ∂f∂XWit

  • 14:

      update the position of wolves using Eq. (8)

  • 15:

     end for

  • 16:

      Calculate the fitness values of all wolves

  • 17:

      Update the Alpha, Beta, and Delta

  • 18:

     for i = 1: number of Omegas

  • 19:

     update the position of Omega wolves using Eqs. (12) to (13)

  • 20:

    end for

  • 21:

    Decrease a→

  • 22:

    it = it + 1;

  • 23:

    end while