基于改进遗传算法的多无人机目标攻击

Jiarui Su, Juntong Qi, Chong Wu, Mingming Wang, Jinjin Guo
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引用次数: 3

摘要

任务分配是多架无人机协同执行任务的主要难题,具有重要的研究价值。针对多无人机任务调度问题,提出了一种基于改进遗传算法的多无人机任务调度方法。首先,根据多无人机攻击目标的应用环境,建立了任务分配的数学模型;其次,针对遗传算法收敛速度慢、易陷入局部极值的问题,将改进的模拟退火算法Metropolis准则引入遗传算法;最后,仿真结果表明,改进的遗传算法可以减少计算量,提高结果质量,更有效地解决多无人机的任务分配问题。
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Multi-UAVs Target Attack Based on Improved Genetic Algorithm
Task assignment is the main conundrum of multiple unmanned aerial vehicles (multi-UAVs) cooperative task execution, which has great research value. To solve the problem of task scheduling for multi-UAVs, a method based on improved genetic algorithm is proposed in this paper. Firstly, a mathematical model of task assignment is established based on the application environment of multi-UAVs attacking targets. Secondly, aiming at the problem of slow convergence speed and easy to fall into local extremum of genetic algorithm, the improved Metropolis criterion of simulated annealing algorithm is introduced into the genetic algorithm. Finally, the simulation results show that the improved genetic algorithm can reduce the amount of calculation, improve the quality of the results, and solve the task assignment problem of multi-UAVs more effectively.
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