TY  - JOUR
AU  - Wang, Rui
AU  - Shan, Yiqing
AU  - Sun, Lianwei
AU  - Sun, Hui
PY  - 2025
DA  - 2025/07/11
TI  - Multi-UAV Cooperative Task Allocation Based on Multi-strategy Clustering Ant Colony Optimization Algorithm
JO  - ICCK Transactions on Intelligent Systematics
T2  - ICCK Transactions on Intelligent Systematics
JF  - ICCK Transactions on Intelligent Systematics
VL  - 2
IS  - 3
SP  - 149
EP  - 159
DO  - 10.62762/TIS.2025.409447
UR  - https://www.icck.org/article/abs/TIS.2025.409447
KW  - UAV
KW  - task allocation
KW  - ant colony algorithm
KW  - K-means clustering
AB  - To address the issues of low solving efficiency and susceptibility to local optima in multi-unmanned aerial vehicle (multi-UAV) task allocation algorithms within urban areas, this study constructs a task allocation model aiming to minimize economic costs for material delivery and reduce the urgency of rescue task demands. A multi-strategy clustering ant colony optimization algorithm (KMACO) is proposed for solution. Specifically, the K-means clustering method is utilized to partition the number of rescue tasks assigned to each UAV. In the ant colony optimization algorithm, a pheromone update strategy and a random evolution strategy are introduced to guide population search directions, thereby enhancing solving efficiency and avoiding local optima. Experimental results demonstrate that the proposed algorithm effectively reduces algorithm running time and operational economic costs while satisfying rescue task urgency requirements. Compared with conventional methods, KMACO shows superior performance in convergence speed and solution quality, thus providing an optimized decision-making framework for emergency rescue operations in complex urban environments.
SN  - 3068-5079
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Wang2025MultiUAV,
  author = {Rui Wang and Yiqing Shan and Lianwei Sun and Hui Sun},
  title = {Multi-UAV Cooperative Task Allocation Based on Multi-strategy Clustering Ant Colony Optimization Algorithm},
  journal = {ICCK Transactions on Intelligent Systematics},
  year = {2025},
  volume = {2},
  number = {3},
  pages = {149-159},
  doi = {10.62762/TIS.2025.409447},
  url = {https://www.icck.org/article/abs/TIS.2025.409447},
  abstract = {To address the issues of low solving efficiency and susceptibility to local optima in multi-unmanned aerial vehicle (multi-UAV) task allocation algorithms within urban areas, this study constructs a task allocation model aiming to minimize economic costs for material delivery and reduce the urgency of rescue task demands. A multi-strategy clustering ant colony optimization algorithm (KMACO) is proposed for solution. Specifically, the K-means clustering method is utilized to partition the number of rescue tasks assigned to each UAV. In the ant colony optimization algorithm, a pheromone update strategy and a random evolution strategy are introduced to guide population search directions, thereby enhancing solving efficiency and avoiding local optima. Experimental results demonstrate that the proposed algorithm effectively reduces algorithm running time and operational economic costs while satisfying rescue task urgency requirements. Compared with conventional methods, KMACO shows superior performance in convergence speed and solution quality, thus providing an optimized decision-making framework for emergency rescue operations in complex urban environments.},
  keywords = {UAV, task allocation, ant colony algorithm, K-means clustering},
  issn = {3068-5079},
  publisher = {Institute of Central Computation and Knowledge}
}