Algorithm 1 Pseudocode of ACO algorithm.

1:                 Initialization part.

2: Configuration of ACO algorithm parameters.

3: Initialization of starting population $\{{\mathbf{x}}^1,{\mathbf{x}}^2,\dots ,{\mathbf{x}}^L\}$ in a random way.

4: Calculation value of the fitness function F for all pheromone spots and sorting them according to their rank (quality).

5:                 Iterative part.

6: for $\mathrm{iteration}i=1,2,\dots ,I$ do

7:  Assignment of probability to pheromone spots according to the Equation (10).

8:  for $\mathrm{ant}m=1,2,\dots ,M$ do

9:    The ant chooses the k-th ($k=1,2,\dots ,L$) solution with probability $p_k$.

10:    for $\mathrm{coordinate}j=1,2,\dots ,n$ do

11:      Using the probability density function (12) in the sampling process, the ant changes the j-th coordinate of the k-th solution.

12:    end for

13:  end for

14:  Calculation the value of the fitness function F for M new solutions.

15:  Adding M new solutions to the set of archive of old, sorting the archive by quality and then rejection of the M worst solutions.

16: end for

17: return best solution ${\mathbf{x}}_{best}$.