Algorithm 1: Solution process of GAO

Start. A certain number of giant armadillos are randomly initialized in the problem-solving space as a population of the algorithm, each representing a candidate solution for the problem. Based on the evaluation of each of the candidate solutions in the objective function and the comparison of the obtained values, the best GAO member is identified as the best candidate solution. In the first phase of the GAO, based on the modeling of the movement of the giant armadillo towards the termite mounds, the position of the GAO members in the problem-solving space and, as a result, the candidate solutions are updated. In the second phase of GAO, based on the modeling of the small displacements of the giant armadillo while digging in termite mounds, the position of GAO members in the problem-solving space and, as a result, candidate solutions are updated. The third and fourth steps are repeated for all GAO members. Based on the comparison of the new evaluated values for the objective function corresponding to the updated candidate solutions, the best candidate solution is identified, updated, and stored. The third to sixth steps are repeated until the last iteration of the algorithm. The best candidate solution obtained during the iterations of the algorithm is presented as the GAO solution for the given problem. End.