Multiscale Cooperative Differential Evolution Algorithm

Algorithm 1

Pseudocode of the MCDE algorithm.

Input: population size NP, dimension D, Maximum number of evaluations MaxFES; the initial scale factor Fm_j, the initial crossover probability CRm_j; weighting factor c = 0.1, power mean n = 4; initialize the population Pop₁, Pop₂, and Pop₃ according to equation (8); let ;

Begin:

 Evolutionary generation G = 0, current evaluation times FES = 0;

 While FES  MaxFES

 For j = 1  3

  If G > 0

   Calculate Fm_j, CRm_j according to equations (13), (14), (16), and (17);

  End if

  For

   Calculate the subpopulation , according to equations (12) and (15);

   Perform mutation strategies according to equations (5), (6), and (7), respectively;

   Perform covariance learning according to equations (10) and (11);

  End for

  For

   If

    ;

    S_F,j = F_i,j, SCR, j = CRi,j;

   Else

    ;

   End if

   FES = FES + NP_j;

  End for

 End for

 , G = G + 1;

 According to the equation (9), the proportion of good individuals and the redistribution of subpopulations were counted;

 End while

End

Output: the population of individual minimum objective function.