基于改进粒子群算法的多无人机任务分配研究

Cooperative Task Allocation for Unmanned Combat Aerial Vehicles Using Improved Particle Colony Algorithm

任务分配问题是多无人机协同控制的关键技术之一。在深入分析多无人机任务分配问题特点的基础上,对现有模型进行了扩展,建立了多无人机协同任务分配的混合整数线性规划(MILP)模型。对现有粒子群算法进行了改进,提出一种具有较强全局搜索能力的多子群多阶段粒子群算法,开展了粒子群算法在多无人机协同任务分配问题中的应用研究,主要针对粒子群算法的编码策略、约束处理、算子选取、参数设置等方面进行相应的调整和改进。最后对算法进行了仿真,仿真结果表明了该方法的有效性。

Task allocation plays an important role in Unmanned Combat Aerial Vehicles＇ （UAVs） cooperative control. Based on analyzing the multiple UAV mission assignment characteristic, this paper expands the existing multiple cooperative UAV mission assignment model, and a mixed integer linear program model for multiple cooperative UAV mission assignment is created. The existing particle colony algorithm is improved and used for solving the model. In the improved PSO, the efficiency of this algorithm in that problem is raised through improvement.