基于混合粒子群优化的巡航导弹低空突防航迹规划

摘要

将模拟退火算法嵌入到粒子群优化(partical swarm optimization,PSO)算法中,并对PSO产生的最优适应值进行重新评价,以此构成混合粒子群优化算法(PSO-SA).将PSO-SA算法应用于巡航导弹的航迹规划,不仅可以避免PSO陷入局部最优,而且能快速有效地完成离线和在线规划任务,获得理想的三维航迹.仿真结果验证了该算法的有效性,且对同一起始位置所规划出的航程较PSO算法短,可有效节约导弹燃料.%A hybrid planning algorithm PSO-SA is presented, which is an integration of the simulated annealing algorithm (SA) and the particle swarm optimization (PSO) algorithm. PSO-SA is used to evaluate the optimal fitness value generated by PSO. PSO-SA used in route planning of cruise missile can avoid the common defect of premature convergence, accomplish the static and dynamic route planning assignment quickly, and produce an ideal 3-D flight path. Simulations demonstrate feasibility of the algorithm. Compared to PSO, PSO-SA achieves a shorter range in the same initial locations, thus, cruise missiles consume less fuel.