This paper proposes a three-stage algorithm based on clustering decomposition and taskallocation-improved clustering planning algorithm (iK-iD-N), aiming at the optimization taskallocation problem of drones in actua...
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This paper proposes a three-stage algorithm based on clustering decomposition and taskallocation-improved clustering planning algorithm (iK-iD-N), aiming at the optimization taskallocation problem of drones in actual application to meet the task demand constraints. The algorithm solves the problem of the number of drones demanded and the initial delivery range of each drone by introducing dual-objective planning into the clustering decomposition. Combining improved Dijkstra algorithm (iK-D) with neighbourhood insertion algorithm into taskallocation, to get high-quality solutions and solve efficiently. Compared with the existing ant colony algorithm, the iK-iD-N algorithm proposed in this paper is more efficient and can obtain the best and stable solutions while evenly distributing tasks. Then it is compared with the improved clustering algorithm combined with the basic iK-D to get better solutions of the iK-iD-N algorithm at any time, and compared with the basic clustering algorithm with the improved task allocation algorithm (K-iD-N) that iK- iD-N can get a better solution with high probability. The thesis also simulates and analyzes the impact of uncertainty requirements on the solutions based on drone demand and taskallocation models, and discusses the impact of drone load capability and endurance capability constraints on the final solutions.
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