基于遗传算法的多无人机对岸打击任务分配研究

Research on Task Assignment for Multi-UAV Shore Strike Based on Genetic Algorithms

无人机在军事领域发展迅速,在对岸打击任务中无人机能够发挥打击作用,但无人机载弹量有限无法单独完成战备工程的打击任务,往往需要多种无人机共同完成打击任务。为实现最大化打击效果,需要找出最优任务分配完任务,针对异构无人机打击对岸目标的任务分配最优求解问题,采用遗传算法求解最优任务分配方案,通过对对岸打击任务分配模型建模,得出遗传算法的适应度函数。基于遗传算法容易“早熟”的问题,在遗传算法迭代过程中调整最优个体遗传数目和变异概率,提高遗传算法效率。最后通过仿真实验验证,改进的遗传算法能有效解决异构无人机打击对岸目标的任务分配问题。

Unmanned aerial vehicles(UAVs)have developed rapidly in the military field,and they can play a role in the on⁃shore attack tasks.However,the limited amount of missiles on the UAVs can not complete the combat readiness project alone,and often need a variety of UAVs to complete the attack tasks together.In order to maximize the hitting effect,it is necessary to find out the optimal task assignment,and to solve the optimal task assignment problem for heterogeneous unmanned aerial vehicles hitting the target on the shore.This paper uses the genetic algorithm to solve the optimal task assignment scheme.By modeling the task as⁃signment model on the shore hitting,the fitness function of the genetic algorithm is obtained.Based on the problem that genetic algo⁃rithm is easy to"premature".In the process of genetic algorithm iteration,the optimal individual genetic number and the probability of variation are adjusted to improve the efficiency of genetic algorithm.Finally,the simulation results show that the improved genetic algorithm can effectively solve the task assignment problem of heterogeneous unmanned aerial vehicles hitting the target on the shore.