为解决无人机侦察航路规划问题,采用文化基因算法(memeticalgorithm,MA)进行求解。以粒子群优化算法作为主搜索策略,采用基于模拟退火的加权法对非劣解进行局部搜索。目标函数综合分析了战术效果、航程、安全性、飞行时间等指标要求,并从环境和无人机自身分析航路规划约束条件。最后对算法性能进行了测试,实验结果表明该文化基因算法比单独使用粒子群优化算法具有更高规划效率,得到的初始侦察航路较优。

In order to solve the reconnaissance route planning problem of unmanned aerial vehicle, the memetic algorithm was adopted, which was widely investigated recently. The particle swarm optimization was selected as the main planning method while the simulated annealing algorithm was applied in the local search. For the objective function, many factors were analyzed including the tactical purpose, flying range, security and time. Under the constraint conditions, not only the environment, but also the UAV itself was taken into consideration. The simulation results indicate that the memetic algorithm based on particle swarm optimization and simulated annealing is more effective than the single PSO, especially for the convergence speed.