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现代防御技术的迅速发展使得无人驾驶飞行器的攻击效果大大下降,无人驾驶飞行器自主编队集群攻击技术已经成为未来战场的关键技术之一,多无人机之间的任务规划算法是保证无人机顺利、高效完成任务的关键.将无人机集群攻击任务规划问题看成是多约束的任务分配过程,建立任务规划模型,结合分布式拍卖机制和生物地理算法对粒子群优化算法的粒子初始化和寻优过程进行改进.根据实际约束条件生成初始粒子,保证了粒子的多样性;在算法优化过程中,利用生物地理算法与粒子群算法对粒子运动进行动态的控制,使得算法具有更好的适应性与稳定性.仿真结果表明运用分布式拍卖机制生物地理粒子群优化算法得到的方案不仅完全满无人机集群攻击任务的要求,而且比传统粒子群优化算法和生物地理粒子群优化算法具有更好的收敛性.
The rapid development of modern defense technology makes the attack performance of unmanned aerial vehicles greatly reduced. The unmanned aircraft autonomous formation of cluster attack technology has become one of the key technologies in the future battlefield. The task planning algorithm between multiple unmanned aerial vehicles is to ensure that nobody The key to the successful and efficient completion of the mission is to consider the UAV cluster mission planning as a multi-constraint task assignment process and establish a mission planning model that combines particle swarm optimization with particle swarm optimization And optimize the process to improve.According to the actual constraints to generate the initial particles, to ensure the diversity of particles in the algorithm optimization process, the use of biogeography and particle swarm optimization of particle dynamics on the dynamic control, making the algorithm has better Adaptability and stability.The simulation results show that the scheme obtained by the distributed auction mechanism of biogeographic particle swarm optimization algorithm is not only completely satisfied with the requirements of the UAV cluster attack task but also has the advantages over the traditional particle swarm optimization and biogeographic particle swarm optimization Better convergence.