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In view of the high computational complexity of task allocation in heterogeneous unmanned aerial vehicle (UAV) swarms and the lack of hard task priority constraints, this study proposes a coalition⁃game⁃based two⁃stage allocation algorithm. The hard priority constraints are introduced while establishing a mathematical model of UAV capabilities and task requirements, and a two⁃stage greedy pre⁃assignment+improved simulated annealing (PC_ISA) algorithm is designed with the framework of coalition game. Stage1 employs a local greedy dynamic algorithm to shrink the infeasible domain, while Stage2 applies the simulated annealing (SA) algorithm with adaptive temperature decay to minimize the coalition cost function. Simulations show that, with 12 UAVs being allocated for three tasks, PC_ISA achieves the best overall performance; after introducing weather disturbance factors, it exhibits the highest stability; and when the numbers of UAVs and tasks are further increased, it delivers the gentlest cost growth. By integrating the ISA algorithm with coalition game and embedding hard task priority constraints, the proposed method provides an effective solution to the task allocation in heterogeneous UAV swarms.
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Basic Information:
DOI：10.16652/j.issn.1004⁃373x.2026.09.002
Citation Information:
[1]Bi Yijun, Wang Xie, Jiang Tian.UAV swarm task allocation based on coalition game and simulated annealing[J].Modern Electronic Technique,2026(9):9-14.DOI:10.16652/j.issn.1004⁃373x.2026.09.002.