基于MapReduce与蚁群优化的航路规划算法

Route Planning Algorithm Based on Map Reduce and Ant Colony Optimization

航路规划是提高无人机生存能力的有效途径,可使其安全、快速到达目的地。为在云计算环境中分布式并行地求解航路规划问题,应用云计算技术提出基于MapReduce和多目标蚁群算法的航路规划算法(RPMA)。设计多目标蚁群算法,并采用多种优化策略对传统算法进行改进。RPMA能预先规划出多条航迹,可根据不同的飞行任务选择不同的航路,并在飞行过程中根据不同需要临时确定合适的飞行航路。仿真实验结果表明,RPMA求解航路问题是可行、有效的,具有较好的收敛性和扩展性,以及对大规模数据的处理能力。

Route planning is an effective way to improve the ability to survive of Unmanned Aerial Vehicle（ UAV） ,for which can make the UAV reach the destination safely and fast. In this paper, the route planning algorithm based on MapReduce and multi-objective Ant Colony Optimization （ ACO ） is put forward, which named RPMA. The multi-objective ACO algorithm is designed in the RPMA and different varieties of optimization strategies are used to improve the RPMA. The RPMA uses cloud computing technology and makes it solve the route planning problems in distributed cloud computing environment and parallel technology. A number of paths are planned in advance. The RPMA is able to make the UAV choose different routes according to different missions or choose the appropriate route according to different temporary needs. The preferable result is got in the simulation experiment,which indicates that the RPMA is an efficient way to solve the route planning problems and has the qualities of convergence and scalability. In addition,the RPMA has the handling abilities of large-scale data.