To improve the computational accuracy of the flatness evaluation under the minimum zone method, an artificial intelligence optimization algorithm called teaching-learning based optimization (TLBO) was applied to the evaluation of the flatness. On the basis of the TLBO algorithm, an adaptive hybrid teaching-learning-based optimization (AHTLBO) algorithm was designed by introducing an adaptive factor and shuffled frog leaping algorithm (SFLA) that are used to improve the search ability of the algorithm, in order to further improve the precision of the algorithm. Finally, the AHTLBO algorithm was verified by seven sets of experiments, and the calculation results were compared with several other common algorithms. The experimental results show that the AHTLBO has higher precision and faster convergence speed in the evaluation of flatness, and it is suitable for high-precision flatness evaluation.