Algorithm 1. Pseudocode of SABO.

Start SABO.

1. Input problem information: variables, objective function, and constraints.

2. Set SABO population size (N) and iterations (T).

3. Generate the initial search agents’ matrix at random using Equation (2). $x_{i,d}\leftarrow l{b}_d+r_{i,d}·\left(u{b}_d-l{b}_d\right)$

4. Evaluate the objective function.

5.   For t = 1 to T

6.     For i = 1 to $N$

7.    Calculate new proposed position for ith SABO search agent using Equation (5). $x_{i,d}^{new}\leftarrow X_i+{\overrightarrow{r}}_i\ast \frac{1}{N}\sum _{j=1}^N\left(X_i {-}_v X_j\right)$

8.    Update ith GAO member using Equation (6). $X_i\leftarrow \{\begin{array}{cc}{X}_i^{new},\hfill & F_i^{new}<F_i\hfill \\ X_i,\hfill & else\hfill \end{array}$

9.     end

10.   Save the best candidate solution so far.

11.   end

12. Output best quasi-optimal solution obtained with the SABO.

End SABO.