Algorithm 2: Pseudocode of GAO
Start GAO.
1.	Input problem information: variables, objective function, and constraints.
2.	Set GAO population size (N) and iterations (T).
3.	Generate the initial population matrix at random using Equation (2). $x_{i,d}\leftarrow l{b}_d+r·\left(u{b}_d-l{b}_d\right)$
4.	Evaluate the objective function.
5.		For $t=1$ to T
6.			For $i=1$ to $N$
7.			Phase 1: Attack on termite mounds (exploration phase)
8.				Determine the termite mounds set for the ith GAO member using Equation (4). $T{M}_i\leftarrow \left\{X_{k_i}:F_{k_i}<F_i \mathrm{and} k_i\ne i\right\}$
9.				Select the termite mounds for the ith GAO member at random.
10.				Calculate new position of ith GAO member using Equation (5). $x_{i,d}^{P1}\leftarrow x_{i,d}+r·\left(ST{M}_{i,d}-I·x_{i,d}\right)$
11.				Update ith GAO member using Equation (6). $X_i\leftarrow \{\begin{array}{c}{X}_i^{P1}, F_i^{P1}<F_i\\ X_i, else\end{array}$
12.			Phase 2: Digging in termite mounds (exploitation phase)
13.				Calculate new position of ith GAO member using Equation (7). $x_{i,d}^{P2}\leftarrow x_{i,d}+\left(1-2r\right)·\frac{\left(u{b}_d-l{b}_d\right)}{t}$
14.				Update ith GAO member using Equation (8). $X_i\leftarrow \{\begin{array}{c}{X}_i^{P2}, F_i^{P2}<F_i\\ X_i, else\end{array}$
15.			end
16.			Save the best candidate solution so far.
17.		end
18.	Output the best quasi-optimal solution obtained with the GAO.
End GAO.