基于改进遗传算法的协同航迹规划方法

A Cooperative Trajectory Planning Method Based on Improved Genetic Algorithm

针对多无人机协同规划问题求解规模大、效率低的问题,提出了一种单-双亲遗传算法(PBGA)求解模型。该算法具有改进的编码方法和一种单双亲结合的进化策略,由单亲遗传算子进行种群进化,由双亲遗传算子跳出局部最优。仿真结果显示,PBGA具有收敛性,在小规模和大规模寻优算例中分别比传统遗传算法减少了70%和64%的收敛代数,对解决多无人机协同问题具有一定的参考价值。

To address the issues of large computation scale and low efficiency of multi-UAV cooperative planning a Partheno-/Bi-Parent Genetic Algorithm(PBGA)solving model is proposed.This algorithm features an improved encoding method and a single-double parent combined evolutionary strategy.The population evolution is carried out by a partheno-genetic operator while the bi-parent genetic operator helps to escape from local optima.Simulation results demonstrate the convergence of PBGA.In small-scale and large-scale optimization scenarios PBGA reduces the convergence iterations by 70%and 64%respectively compared with the traditional genetic algorithm.This approach holds significant reference value for addressing multi-UAV cooperative problems.