Charge optimization based on BP neural networks and genetic algorithm

Many factors can affect the performance of LSC, and most of these effects are nonlinear and complicated. The optimization design of charge structures is noticed widely. For efficient optimization methods, the wedge charge respectively was considered as research objects. First, the orthogonal experimental method was used to design different programs, and the ANSYS/LS-DYNA was used to obtain simulation results. Then, the structural parameters and the jet velocity maximum were set as the input and output of BP neural networks for training, and the prediction result was set as the fitness. Finally, the genetic algorithm was applied to search best structural parameters and jet velocity maximum of the wedge charge respectively. The study results indicated that this method can combine advantages of the orthogonal experimental method, BP neural networks and genetic algorithm for efficient optimization of charge structures.