Three‐stage improved algorithm based on clustering decomposition and its application in drone demand and task allocation.
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| Title: | Three‐stage improved algorithm based on clustering decomposition and its application in drone demand and task allocation. |
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| Authors: | Liu, Zhengyuan, Wang, Qinghua |
| Source: | IET Communications (Wiley-Blackwell); Oct2022, Vol. 16 Issue 16, p1957-1972, 16p |
| Subject Terms: | ANT algorithms, DRONE aircraft delivery, ALGORITHMS, IMPACT loads, TASKS |
| Abstract: | This paper proposes a three‐stage algorithm based on clustering decomposition and task allocation—improved clustering planning algorithm (iK‐iD‐N), aiming at the optimization task allocation 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 task allocation, 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 task allocation models, and discusses the impact of drone load capability and endurance capability constraints on the final solutions. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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