基于种群熵粒子群优化算法的上升段交会弹道优化设计

Launch Vehicle Ascent Rendezvous Trajectory Optimum Design Based on Population Entropy Based Particle Swarm Optimization

应用种群熵粒子群优化(Population Entropy based Particle Swarm Optimization,EPSO)算法研究运载火箭上升段交会弹道优化设计问题。以运载火箭和目标飞行器在交会时刻距离最小为目标函数,建立运载火箭上升段交会弹道优化模型,同时分别采用EPSO优化算法和传统粒子群优化算法进行求解。仿真结果表明,EPSO算法能够有效解决运载火箭上升段交会弹道优化问题,平均交会位置误差为8.33 m,较传统粒子群算法减少了149.37 m,平均搜索速度较传统算法提高了27%。EPSO算法收敛精度高,搜索速度快,更适用于解决上升段交会弹道优化这样的复杂约束优化问题。

The paper researched the optimum design of launch vehicle ascent rendezvous trajectory using population entropy based particle swarm optimization （EPSO） algorithm. Take the minimum distance of launch vehicle and target aircraft at intersection point as the objective function, an optimization model of launch vehicle ascent rendezvous trajectory is established and at the same time solved by EPSO algorithm and traditional particle swarm optimization algorithm respectively. The simulation result indicates that the EPSO algorithm can solve the optimization problem of launch vehicle ascent rendezvous trajectory effectively, the average error of rendezvous position is 8.33 m, reduced 149.37 m and the average search speed is improved 27% compared with the traditional particle swarm optimization algorithm. The EPSO algorithm is more suitable to solve complex constraint optimization problem as optimum design of launch vehicle ascent rendezvous trajectory because of its higher convergence accuracy and faster search speed.