To adjust the parameters that influence the appearance of defects in the steel in the continuous casting mold, a particle swarm optimization algorithm (PSO) was developed using process models. The study considered multiple objectives with multiple restrictions and the results were compared with those reported by a teaching-learning-based optimization algorithm (TLBO). We conclude that the PSO has a good capacity to determine the parameters of the mold, and with it, it is possible to achieve an optimal solution without requiring great computational efforts.

To adjust the parameters that influence the appearance of defects in the steel in the continuous casting mold, a particle swarm optimization algorithm (PSO) was developed using process models. The study considered multiple objectives with multiple restrictions and the results were compared with those reported by a teaching-learning-based optimization algorithm (TLBO). We conclude that the PSO has a good capacity to determine the parameters of the mold, and with it, it is possible to achieve an optimal solution without requiring great computational efforts.