Algorithm 1 Pseudo-code of the TSO approach.
Start the TSO algorithm.
1.	Determine the range of decision variables, constraints and objective function of the problem.
2.	Create the initial population at random.
3.	Evaluate the objective function based on the initial population.
4.	For t = 1:T, with t being iteration number and T the maximum iteration:
5.	Update the good group.
6.	For i = 1:N, with N being the number of population members;
7.	For d = 1:m, with d being the contour and m the number of variables:
8.	Select the j’-th good member.
9.	Stage 1: Update ${x^{\prime }}_i^d$ based on (1).
10.	End for d = 1:m.
11.	Update $\overrightarrow{X_i}$ based on (2).
12.	For d = 1:m:
13.	Select the k’-th good member, with k ≠ j.
14.	Stage 2: Update ${x^{\prime }}_i^d$ based on (3).
15.	End for d = 1:m.
16.	Update $\overrightarrow{X_i}$ based on (4).
17.	End for i = 1:N.
18.	Save the best quasi-optimal solution.
19.	End for t = 1:T.
20.	Print the best quasi-optimal solution obtained by the TSO algorithm.
End the TSO algorithm.