Calibrating the microparameters of DEM models using the ant colony optimization algorithm and the optimal hyperparameters

Particle flow code (PFC) is a typical discrete element method (DEM) that is commonly used to simulate the mechanical behaviour of granular materials. However, the micro-parameters should be calibrated when the PFC is used for numerical simulation, and the commonly used trial-and-error techniques have many drawbacks (user dependence and high computational costs). In this work, to obtain the proper set of micro-parameters, a calibration framework is proposed that uses the ant colony optimization algorithm. In contrast to other micro-parameter calibration techniques, no dataset is prepared, and the calibration process is controlled by the Python script language without any human interference. Finally, the proposed method is verified by two examples, and the numerical simulation results indicate that the proposed method is effective. To reduce the number of iterations for obtaining the micro-parameters, the simulated annealing algorithm is used, and the optimal hyperparameters for the ant colony calibration method are obtained. By using these hyperparameters, the number of calibration iterations is reduced.