Multi-UAV Task Allocation Method for Road Emergency Rescue Based on Improved Multi-Objective Genetic Algorithm

Xiaoyu Wang; Yangshan Tang; Ying Li

doi:10.63313/AJET.9062

Authors

Xiaoyu Wang School of Automotive and Traffic Engineering, Liaoning University of Technology, Jinzhou 121000, China Author
Yangshan Tang School of Automotive and Traffic Engineering, Liaoning University of Technology, Jinzhou 121000, China Author
Ying Li School of Automotive and Traffic Engineering, Liaoning University of Technology, Jinzhou 121000, China Author

DOI:

https://doi.org/10.63313/AJET.9062

Keywords:

UAV, Emergency Rescue, Task Allocation, Multi-objective Genetic Algorithm, Capacitated Vehicle Routing Problem

Abstract

To enhance the operational efficiency of multi-UAV material delivery in emergency rescue scenarios, this paper establishes a mathematical model for multi-UAV task allocation and proposes an improved hybrid multi-objective genetic algorithm, namely the AC-MOGA algorithm. Firstly, the multi-UAV material delivery task allocation problem is elaborated. A mathematical model is constructed with three optimization objectives: minimizing the total flight distance of UAVs, reducing the maximum task completion time, and optimizing the weighted total response time. Secondly, Logistic chaotic mapping and adaptive strategies are introduced to modify the multi-objective genetic algorithm. The improvements are implemented in population initialization, crossover and mutation operations to construct the proposed algorithm for problem solving. Finally, based on six groups of VRPLIB benchmark instances, numerical simulations are carried out on the MATLAB platform to obtain the task allocation schemes derived by the AC-MOGA algorithm. Comparative analyses with a state-of-the-art improved genetic algorithm are conducted on identical benchmark instances. The results verify the effectiveness and feasibility of the AC-MOGA algorithm for solving vehicle routing problems.

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