Abstract
Multi-drone logistics transportation and distribution have gradually become the focus of future logistics. So as to solve the multi-UAV logistics planning problem, the goal of this paper is to minimize the cost of take-off and variable-load flight, and builds a multi-UAV logistics planning model with constraints such as flight distance and logistics load. The Improved Genetic Simulated Annealing Algorithm(IGSAA) adds the SA mechanism on the basis of the classical GA, selectively adds non-optimal solutions and non-constrained solutions, and adopts the Improved Large Neighborhood Search Algorithm (ILNSA) to generate special algorithms with three strategies. By continuously updating the population iteration and selectively adding the probability according to the temperature change, the optimal UAV logistics planning is finally optimized. Finally, the simulation results close to reality show that compared with the initial random solution and the classical genetic algorithm, the improved genetic simulated annealing algorithm proposed in this paper reduces the flight cost of UAV by 16.78% and 8.49%, respectively, and can obtain lower transportation costs. Drone logistics solution.
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Acknowledgment
This work was funded by National Natural Science Foundation of China (No. 51979275), Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities, MNR (No. KFKT-2022-05), Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources (No. KF-2021-06-115), Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (No. VRLAB2022C10), and 2115 Talent Development Program of China Agricultural University.
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Xue, Z., Chen, J., Cao, Y., Zhang, Z., Liu, X. (2023). Multi-UAV Logistics Planning Problem Based on Improved Genetic Simulated Annealing Algorithm. In: Yan, L., Duan, H., Deng, Y. (eds) Advances in Guidance, Navigation and Control. ICGNC 2022. Lecture Notes in Electrical Engineering, vol 845. Springer, Singapore. https://doi.org/10.1007/978-981-19-6613-2_357
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DOI: https://doi.org/10.1007/978-981-19-6613-2_357
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