Adaptive PSO-SO algorithm with Sobol sequence for aerodynamic physical parameter identification of projectiles

In the domain of aerodynamic physical parameter identification, conventional optimization algorithms are often limited by falling into local optima. To overcome this limitation, a novel adaptive PSO-SO algorithm based on Sobol sequences (SAPSO-SO) algorithm is proposed in this study. The algorithm integrates particle swarm optimization algorithms and snake optimization algorithms, utilizing Sobol sequences for initialization, which enhances the global search and local development ability of the algorithm by adaptively adjusting the inertia weights and learning factors. In addition, this study introduced a local optimal discriminant mechanism and a local search function to further enhance the optimization performance of the algorithms. In this study, the small interval constant method was used to subdivide the trajectory, relying on the three-degree-of-freedom ballistic model to identify the starting ballistic parameters and aerodynamic physical parameters of each small interval. The performances of the snake optimization algorithm, particle swarm optimization algorithm, C-K method, and SAPSO-SO algorithm in the identification of ballistic physical parameters were compared using the full ballistic simulation data of a high-speed rotating projectile as measurement data. The results show that the SAPSO-SO algorithm demonstrates excellent accuracy and effectiveness, especially in noisy simulation data, where its recognition accuracy is improved by 7.79% over the C-K method, highlighting its superior anti-noise performance and global optimization capability. It is comprehensively analyzed that the SAPSO-SO algorithm has strong global optimization potential in theory and shows a high degree of accuracy and stability in practical applications, independent of the selection of initial parameters.