针对动态目标的多无人机协同组合差分进化搜索方法

针对多无人机动态目标协同搜索问题,提出一种组合差分进化无人机协同搜索航迹规划方法.建立动态目标协同搜索环境信息图模型及无人机运动模型.基于改进差分蝙蝠算法和自适应差分进化算法,设计基于种群数量自适应分配的组合框架,将差分进化算法中的变异、交叉和选择机制引入蝙蝠算法,构建组合差分进化算法的协同搜索算法,并对无人机动态目标协同搜索的航迹进行优化.针对待搜索目标轨迹随机多变且具有规避侦察特性的现实场景,建立可回访数字信息图和自适应目标搜索增益函数,从而提高无人机对动态目标的捕获能力.最后,通过仿真结果验证所提出的无人机动态目标协同搜索算法的有效性.

A composite differential evolution algorithm for multi-UAV cooperative dynamic target search

To solve the problem of multi-UAV cooperative search for dynamic targets, a composite differential evolution algorithm is proposed for multiple UAVs to perform cooperative dynamic target search. First, the environment information graph model for the cooperative dynamic target search and UAV model are established. Then, based on the improved differential bat algorithm and the adaptive differential evolution algorithm, a combinatorial framework relied on population adaptive allocation is designed. By introducing the mutation, crossover and selection mechanisms of the differential evolution algorithm to the bat algorithm, a cooperative search algorithm combined with the differential evolution algorithm is constructed. For the real scenario where the trajectories of dynamic targets are random and unpredictable and have reconnaissance evasion trait, the retrievable digital information graph and the adaptive target search gain function are established to enhance the capabilities of UAVs to capture dynamic targets. Finally, simulation results demonstrate the effectiveness of the proposed UAV cooperative dynamic target search algorithm.