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. 2025 Feb 23;25(5):1366. doi: 10.3390/s25051366
Algorithm 1 Pseudocode of the ISBOA
  •   1:

    Initialize problem settings: DimublbPopSize (N), MaxIt (T), CurrIter (t)

  •   2:

    Initialize the population randomly

  •   3:

    for t=1 to T do

  •   4:

        Update Candidate Solution xbest

  •   5:

        The Cossboo cuckoo mutated:

  •   6:

        for i=1 to N do

  •   7:

            Compute the Gaussian mutation of the ith Candidate Solution using Equation (13)

  •   8:

            Calculate the cuckoo’s random move step length for the ith Candidate Solution using Equations (14) and (15).

  •   9:

            Calculate and update the new state of the ith Candidate Solution using Equations (16) and (17).

  • 10:

        end for

  • 11:

        SBOA main part:

  • 12:

        for i=1 to N do

  • 13:

            if t<13T then

  • 14:

               Calculate new status of the ith Candidate Solution using Equation (18)

  • 15:

               Update the ith Candidate Solution using Equation (17)

  • 16:

            else if 13T<t<23T then

  • 17:

               Calculate new status of the ith Candidate Solution using Equation (19)

  • 18:

               Update the ith Candidate Solution using Equation (17)

  • 19:

            else

  • 20:

               Calculate new status of the ith Candidate Solution using Equation (20)

  • 21:

               Update the ith Candidate Solution using Equation (17)

  • 22:

            end if

  • 23:

        end for

  • 24:

        for i=1 to N do

  • 25:

            if r<0.5 then

  • 26:

               Calculate new status of the ith Candidate Solution using C1 in Equation (22)

  • 27:

            else

  • 28:

               Calculate new status of the ith Candidate Solution using C2 in Equation (22)

  • 29:

            end if

  • 30:

            Update the ith Candidate Solution using Equation (17)

  • 31:

        end for

  • 32:

        Smooth Exploration System:

  • 33:

        for i=1 to N do

  • 34:

            Calculate the sampling rate of ith Candidate Solution using Equation (23)

  • 35:

            Update the ith Candidate Solution via random crossover in Equation (24).

  • 36:

            Update the ith Candidate Solution via sequence mutation in Equation (25).

  • 37:

        end for

  • 38:

        Save the best candidate solution so far

  • 39:

    end for

  • 40:

    Output: The best solution obtained by ISBOA for the given optimization problem return Best solution