Adaptive large neighborhood search algorithm for the Unmanned aerial vehicle routing problem with recharging

Abstract

The applications of Unmanned aerial vehicles (UAVs) in both civilian and military fields are drawing increasing attention recently. This paper investigates a new routing problem of small UAVs for information collection with time windows, where UAVs can be recharged at platforms (e.g. ground vehicles or stations) distributed in a given area. Different from the previous works on UAV routing, the UAVs are allowed to partially recharge their batteries according to the requirement in the following route. A mixed integer nonlinear programming model is developed to formulate the problem, where the overall time for completing all targets’ observation and the number of UAVs are minimized. An improved adaptive large neighborhood search (ALNS) algorithm with simulated annealing strategies is designed, and a recharging platform insertion heuristic is developed to determine the recharging strategy and construct feasible solutions. To verify the efficiency of the proposed algorithms, a set of new benchmark instances are designed based on the well-known Solomon data set and solved. The computational results are compared with those obtained by the ant colony optimization and variable neighborhood search, which shows that ALNS performs significantly better and stable. Furthermore, experimental analysis indicates that important advantages can be obtained through introducing the recharging strategy for small UAVs.