摘要
为解决复杂约束环境下大规模无人战斗机(UCAV)编队队形优化问题,提出基于双层规划模型的队形优化求解算法.以大规模UCAV编队空对地饱和打击作战场景为例,建立UCAV编队作战上层规划模型,通过采用离散粒子群-模拟退火(DPSO-SA)算法进行求解,得到执行每个任务的UCAV编号和最优队形;根据现有的编队作战队形库,建立编队中UCAV站位下层规划模型,通过采用遗传算法进行求解,得到UCAV在队形中的位置.仿真结果表明:在上层规划模型中引入改进模拟退火算法,可以解决离散粒子群算法易陷入局部极小值的问题;设计双层规划模型,可以解决DPSO-SA算法后期收敛速度慢的问题.相对于单层规划模型,双层规划模型求解大规模UCAV编队队形优化问题收敛速度更快,寻优效果更好.
In order to optimize the formation of large-scale unmanned combat aircraft vehicle(UCAV) in complex constraint environment, an algorithm for formation optimization based on bi-level programming model was proposed. According to the existing UCAV formation combat mode of air to ground, the upper-level model of UCAV formation in combat environment was established. The discrete particle swarm optimization and simulated annealing(DPSO-SA) algorithm was used to obtain the number of UCAV and the best formation of each task. According to the existing formation library, the lower-level model of the UCAV location was built, and the UCAV position in the formation was obtained by using the genetic algorithm. The simulation results show that the improved simulated annealing algorithm can solve the problem that the discrete particle swarm optimization is easy to fall into local minimum, and the slow convergence rate of DPSO-SA can be solved with the design of a bi-level programming model. Compared with the single-level programming model, the bi-level programming model has faster convergence speed and better optimization effect on solving large-scale UCAV formation optimization problems.
引文
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