基于改进粒子群优化算法的多弹打击面目标瞄准点优化方法

An improved particle swarm optimization (IPSO) algorithm is proposed to optimize the optimal aiming point of multiple projectiles against complex-shaped surface targets. When constructing an aiming point selection model, the influences of complex factors such as surface target area shape, regional correlation,ammunition power area,ammunition hit accuracy,cumulative damage and multi-projectiles combined damage on target damage effect are considered comprehensively. Particle swarm optimization (PSO) algorithm is improved by preassigning the particle positions and introducing the particle activation energy,which not only improves the convergence speed of the algorithm but also ensures the global search ability. The proposed algorithm is verified through typical complex target test cases. The results show that, compared with Monte Carlo algorithm,PSO algorithm and improved grey wolf optimization algorithm,the IPSO algorithm has a better ability to select aiming points,and increases the average damage yield by 4. 3%. And the average computation time for aiming point selection is only 1/4-1/3 of that of traditional optimization algorithms.,which has obvious advantages in damage income and computing efficiency.