Algorithm 2 Pseudo-code of the DE.

Input:  $\mathrm{N}$: population size; $\mathrm{Dim}$: problem dimension; $\mathrm{Tmax}\_\mathrm{iter}$: maximum number of iterations; $\mathrm{F}$: scaling factor; $\mathrm{CR}$: crossover probability; Output:  The position of the best individual and its fitness value;   1:Initialize the population $\mathrm{X}$ randomly;   2:Calculate the fitness value of X;   3:while stopping condition is not met do   4:      for ${\mathrm{x}}_{\mathrm{i}}^{\mathrm{t}}$ do   5:         The mutation vector ${\mathrm{v}}_{\mathrm{i}}^{\mathrm{t}}$ is generated using the mutation operation shown in Equation (22) for individual ${\mathrm{x}}_{\mathrm{i}}^{\mathrm{t}}$;   6:      end for   7:      for ${\mathrm{x}}_{\mathrm{i}}^{\mathrm{t}}$ do   8:         A test vector ${\mathrm{tr}}_{\mathrm{i},\mathrm{j}}^{\mathrm{t}}$ is generated for individual ${\mathrm{x}}_{\mathrm{i}}^{\mathrm{t}}$ and variance vector ${\mathrm{v}}_{\mathrm{i}}^{\mathrm{t}}$ using the crossover operation shown in Equation (25);   9:      end for 10:      Calculate the fitness value of the function for each trial vector ${\mathrm{tr}}_{\mathrm{i},\mathrm{j}}^{\mathrm{t}}$; 11:      for ${\mathrm{x}}_{\mathrm{i}}^{\mathrm{t}}$ do 12:         Generate the next generation of individuals ${\mathrm{x}}_{\mathrm{i}}^{\mathrm{t}+1}$ using the selection operation for individual ${\mathrm{x}}_{\mathrm{i}}^{\mathrm{t}}$ and trial vector ${\mathrm{tr}}_{\mathrm{i},\mathrm{j}}^{\mathrm{t}}$; 13:      end for 14:end while