Internal Ballistic Optimization Design Based on Improved Particle Swarm Optimization Title

In order to optimize the interior ballistic parameters of artillery, an improved particle swarm optimization method for interior ballistic parameters is proposed by improving the learning factor, inertia weight coefficient and introducing simulated annealing. By adopting linear asynchronous change strategy to the learning factor, the weight coefficient of non-linear change is constructed, which reduces the parameter sensitivity of the optimization model and speeds up the convergence speed. Meanwhile, the Metropolis rule is applied to avoid the drawbacks of traditional particle swarm algorithm easily falling into local optimum. Using the optimization method in this paper, the projectile velocity is increased from 994.15 m/s of the initial scheme to 1016.76 m/s, which shows that the method meets the practical requirements of interior ballistic design engineering and provides a new idea for the optimum design method of barrel weapons.