交互策略改进MOFA进化的多UAV协同航迹规划

针对无人机(unmanned aerial vehicle,UAV)多目标优化协同航迹规划方法中Pareto最优解集规模随迭代增长,难以选择适合UAV任务特点的协同航迹等问题,提出一种基于交互策略改进多目标萤火虫(multi-objective firefly algorithm,MOFA)进化的多UAV协同航迹规划方...

Multi-UAV cooperative path planning based on improved MOFA evolution of interactive strategy

In view of the problem that the number of Pareto optimal solution sets in the unmanned aerial vehicle (UAV) multi objective path planning method increases with iteration, it is difficult to choose the cooperative path suitable for the task, a multi-UAV cooperative path planning based on improved multi-objective firefly algorithm (MOFA) evolution of interactive strategy is proposed. First, the variable decomposition strategy is used to decompose large scale variables in the firefly algorithm into multiple subpopulations to reduce algorithm search complexity. Then, the Tent chaos initialization strategy and multiple population cycle split merge strategy are used to improve the search performance of the algorithm. Bipolar preference dominance is used and designed the cooperation index to select the cooperative path suitable for the task in the Pareto optimal solution set. The simulation experiments show that the proposed algorithm can accurately find the optimal route planning scenarios that satisfies the focus and synergy according to the task setting, and demonstrate that the efficiently of the proposed algorithm.