从图13可知，目标函数值在第8次迭代时收敛，收敛在了0.0105967

It can be seen from Figure 13 that the objective function value converges at the 8th iteration and converges to 0.0105967. Compared with the experimental data without adding uncertainty, the objective function value has increased significantly. The objective function value shows that the temperature field data obtained by the coupling algorithm inversion has an error of 1.059% compared with the experimental measured data with an increase of 2% uncertainty. This also shows that the inverse problem solver based on particle swarm optimization algorithm has better anti-interference ability.

It can be seen from Figure 13 that the value of the objective function converged at 0.0105967 in the 8th iteration. Compared with the experimental data without uncertainty, the value of the objective function increased significantly. The value of the objective function shows that the error of the temperature field data retrieved by the coupling algorithm is 1.059% compared with the experimental data with an increase of 2% uncertainty. This also shows that the inverse problem solver based on particle swarm optimization algorithm has good anti-interference ability.

As can be seen from Figure 13, the objective function value converged to 0.0105967 in the 8th iteration, and compared with the experimental data without adding uncertainty, the objective function value increased greatly. The objective function value shows that the error of the temperature field data retrieved by the coupling algorithm is 1.059% compared with the experimental data with 2% increased uncertainty. It also shows that the inverse problem solving program based on particle swarm optimization algorithm has good anti-interference ability.