In recent years,Unmarred Aerial Vehicles(UAVs)have been extensively employed for reconnaissance missions.Our focus is prioritizing reconnaissance of high-priority targets while minimizing the flight duration when UAV ...In recent years,Unmarred Aerial Vehicles(UAVs)have been extensively employed for reconnaissance missions.Our focus is prioritizing reconnaissance of high-priority targets while minimizing the flight duration when UAV power is constrained.We introduce a framework,which integrates Deep-Q Network(DQN)into the Adaptive Large Neighborhood Search(ALNS)metaheuristic algorithm,called DQN-ALNS,to optimize the process through the current solution’s search state.Specifically,the agent is utilized to select the destroy-repair operators to update a new solution,thereby iteratively optimizing the UAV reconnaissance routes.Experimental results reveal that DQN-ALNS achieves superior solutions and faster convergence than other comparison algorithms.The algorithm leverages the exploratory potential of the current solution and demonstrates robust stability.The final sensitivity analysis showcases that reconnaissance missions with high priority are better accomplished when the UAV power is moderate and the target priority is concentrated at smaller values.展开更多
基金supported by the National Natural Science Foundation of China(Nos.72271241 and 72301290)the Hunan Key Laboratory of Intelligent Decision-Making Technology for Emergency Management(No.2020TP1013).
文摘In recent years,Unmarred Aerial Vehicles(UAVs)have been extensively employed for reconnaissance missions.Our focus is prioritizing reconnaissance of high-priority targets while minimizing the flight duration when UAV power is constrained.We introduce a framework,which integrates Deep-Q Network(DQN)into the Adaptive Large Neighborhood Search(ALNS)metaheuristic algorithm,called DQN-ALNS,to optimize the process through the current solution’s search state.Specifically,the agent is utilized to select the destroy-repair operators to update a new solution,thereby iteratively optimizing the UAV reconnaissance routes.Experimental results reveal that DQN-ALNS achieves superior solutions and faster convergence than other comparison algorithms.The algorithm leverages the exploratory potential of the current solution and demonstrates robust stability.The final sensitivity analysis showcases that reconnaissance missions with high priority are better accomplished when the UAV power is moderate and the target priority is concentrated at smaller values.
