Differential evolution with sensitivity analysis and the Powell's method for crowd model calibration

This paper proposes a novel evolutionary algorithm named differential evolution with sensitivity analysis and the Powell's method (DESAP) for model calibration. The proposed DESAP owns three main features. First, an entropy-based sensitivity analysis operation is introduced to dynamically identify important parameters of the model as evolution progresses online. Second, the Powell's method is performed periodically to fine-tune the important parameters of the best individual in the population. Finally, in each generation, the evolutionary operators are performed on a small number of better individuals in the population. These new search mechanisms are integrated into the differential evolution framework to improve the search efficiency. To validate its effectiveness, the proposed DESAP is applied to two crowd model calibration cases. The results demonstrate that the proposed DESAP outperforms several model calibration methods in terms of solution accuracy and search efficiency.