Collaborative multi-task assignment of heterogeneous UAVs based on hybrid strategies based multi-objective particle swarm

doi:10.3785/j.issn.1008-973X.2025.04.018

Journal of ZheJiang University (Engineering Science)

2025, Vol. 59

Issue (4): 821-831 DOI: 10.3785/j.issn.1008-973X.2025.04.018

Collaborative multi-task assignment of heterogeneous UAVs based on hybrid strategies based multi-objective particle swarm

Yu WANG1(

),Chunrong MA2,Mingyue ZHAO1

1. College of Automation, Shenyang Aerospace University, Shenyang 110136, China
2. Aerospace Shenzhou Aerial Vehicle Ltd., Tianjin 300450, China

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Abstract

Aiming at the problem of collaborative multi-task assignment of heterogeneous UAVs under multiple constraints, a three-objective optimization model was constructed, which considered the UAV flight distance cost, time cost, combat effectiveness and multiple constraints. A multi-objective particle swarm optimization algorithm based on hybrid strategies was proposed to solve the model. An average action efficiency index of ammunition was proposed to evaluate the task strike efficiency, and considering the possibility of deadlock during task execution, a calculation of waiting time was proposed in the process of modeling. In order to solve the problem that traditional particle swarm optimization falls into local optimality, and ensure that feasible solutions satisfying constraints are searched, a constraint-based particle dynamic optimal initialization strategy, a dominance relationship-based advantageous individual selection strategy, and a task-based small module particle update and correction strategy were proposed, respectively. The overall performance of the algorithm in terms of convergence accuracy and diversity was effectively improved by these strategies. The validity of the model and the algorithm was verified through multi-scenario simulation experiments and ablation experiments. Results show that the solution sets obtained by the proposed algorithm are more convergent, diverse and evenly distributed than the comparative algorithms, and the collaborative multi-task assignment of heterogeneous UAVs is efficiently realized by the proposed algorithm.

Key words： heterogeneous UAVs task assignment multi-objective optimization particle swarm hybrid strategy

Received: 21 February 2024 Published: 25 April 2025

CLC:

TP 393

Fund: 国家自然科学基金资助项目（61906125, 62373261）；辽宁省高校基本科研业务费项目（LJ232410143020, LJ212410143047）.

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	Yu WANG
	Chunrong MA
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Cite this article:

Yu WANG,Chunrong MA,Mingyue ZHAO. Collaborative multi-task assignment of heterogeneous UAVs based on hybrid strategies based multi-objective particle swarm. Journal of ZheJiang University (Engineering Science), 2025, 59(4): 821-831.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2025.04.018 OR https://www.zjujournals.com/eng/Y2025/V59/I4/821

基于混合策略多目标粒子群的异构无人机协同多任务分配

针对多约束条件下的异构无人机协同多任务分配问题，构建综合考虑航程代价、时间代价、作战效能和多个约束的三目标优化模型，提出基于混合策略的多目标粒子群优化算法. 为了评价任务打击效率同时考虑死锁难题，在建模过程中提出弹药平均作用效能指标和等待时间计算. 为了解决传统粒子群算法易陷入局部最优的问题，确保始终搜索到满足约束的可行解，分别提出基于约束的粒子动态优选初始化策略、基于支配关系的优势个体选择策略和基于任务的小模块粒子更新及修正策略，有效提升算法在收敛精度及多样性方面的综合性能. 通过多场景下的仿真、消融实验验证模型及算法的有效性. 结果表明，相较对比算法，所提算法得到的解集更收敛、多样，分布更均匀，能够高效实现异构无人机的协同多任务分配.

关键词： 异构无人机, 任务分配, 多目标优化, 粒子群, 混合策略

Tab.1 Parameter of task assignment model

Fig.1 Flow chart of hybrid strategies based multi-objective particle swarm optimization algorithm

Fig.2 Example of particle dynamic initialization coding

Fig.3 Flowchart of advantageous individual selection strategy

Fig.4 Example of task-based particle learning with small modules

Fig.5 Example of task-based particle variation for small modules

Fig.6 Correction results of particle based on constraints

实例	无人机	任务分配结果
6×3	$ u_{\text{O}}^{\text{1}} $	$ {T_1}\left( {{M_{\text{O}}}} \right) \to {T_1}\left( {{M_{\text{E}}}} \right) $
	$ u_{\text{O}}^{\text{2}} $	$ {T_2}\left( {{M_{\text{O}}}} \right) \to {T_2}\left( {{M_{\text{E}}}} \right) \to {T_3}\left( {{M_{\text{O}}}} \right) \to {T_3}\left( {{M_{\text{E}}}} \right) $
	$ u_{\text{A}}^{\text{1}} $	$ {T_1}\left( {{M_{\text{A}}}} \right) $
	$ u_{\text{A}}^{\text{2}} $	$ {T_2}\left( {{M_{\text{A}}}} \right) $
	$ u_{\text{A}}^{\text{3}} $	$ {T_3}\left( {{M_{\text{A}}}} \right) $
10×6	$ u_{\text{O}}^{\text{1}} $	$ {T_6}\left( {{M_{\text{O}}}} \right) \to {T_1}\left( {{M_{\text{O}}}} \right) \to {T_1}\left( {{M_{\text{E}}}} \right) \to {T_6}\left( {{M_{\text{E}}}} \right) $
	$ u_{\text{O}}^{\text{2}} $	$ {T_2}\left( {{M_{\text{O}}}} \right) \to {T_4}\left( {{M_{\text{O}}}} \right) \to {T_3}\left( {{M_{\text{O}}}} \right) \to {T_4}\left( {{M_{\text{E}}}} \right) $
	$ u_{\text{O}}^{\text{4}} $	$ {T_2}\left( {{M_{\text{O}}}} \right) \to {T_5}\left( {{M_{\text{O}}}} \right) \to {T_3}\left( {{M_{\text{E}}}} \right) $
	$ u_{\text{O}}^{\text{5}} $	$ {T_2}\left( {{M_{\text{O}}}} \right) \to {T_2}\left( {{M_{\text{E}}}} \right) \to {T_5}\left( {{M_{\text{E}}}} \right) $
	$ u_{\text{A}}^{\text{1}} $	$ {T_2}\left( {{M_{\text{A}}}} \right) $
	$ u_{\text{A}}^{\text{3}} $	$ \begin{gathered} {T_6}\left( {{M_{\text{A}}}} \right) \to {T_1}\left( {{M_{\text{A}}}} \right) \to {T_4}\left( {{M_{\text{A}}}} \right) \to {T_3}\left( {{M_{\text{A}}}} \right) \to {T_5}\left( {{M_{\text{A}}}} \right) \end{gathered} $
15×10	$ u_{\text{O}}^{\text{1}} $	$ \begin{gathered} {T_2}\left( {{M_{\text{O}}}} \right) \to {T_1}\left( {{M_{\text{O}}}} \right) \to {T_6}\left( {{M_{\text{O}}}} \right) \to {T_4}\left( {{M_{\text{E}}}} \right) \to \\ {T_1}\left( {{M_{\text{E}}}} \right) \to {T_6}\left( {{M_{\text{E}}}} \right) \\ \end{gathered} $
	$ u_{\text{O}}^{\text{2}} $	$ \begin{gathered} {T_{10}}\left( {{M_{\text{O}}}} \right) \to {T_3}\left( {{M_{\text{O}}}} \right) \to {T_8}\left( {{M_{\text{O}}}} \right) \to {T_9}\left( {{M_{\text{O}}}} \right) \to \\ {T_5}\left( {{M_{\text{E}}}} \right) \to {T_7}\left( {{M_{\text{E}}}} \right) \to {T_9}\left( {{M_{\text{E}}}} \right) \\ \end{gathered} $
	$ u_{\text{O}}^{\text{3}} $	$ {T_4}\left( {{M_{\text{O}}}} \right) \to {T_6}\left( {{M_{\text{E}}}} \right) $
	$ u_{\text{O}}^{\text{4}} $	$ {T_2}\left( {{M_{\text{O}}}} \right) \to {T_7}\left( {{M_{\text{O}}}} \right) \to {T_4}\left( {{M_{\text{O}}}} \right) \to {T_2}\left( {{M_{\text{E}}}} \right) $
	$ u_{\text{O}}^{\text{5}} $	$ {T_7}\left( {{M_{\text{O}}}} \right) \to {T_3}\left( {{M_{\text{E}}}} \right) \to {T_8}\left( {{M_{\text{E}}}} \right) $
	$ u_{\text{O}}^{\text{7}} $	$ {T_2}\left( {{M_{\text{O}}}} \right) \to {T_5}\left( {{M_{\text{O}}}} \right) $
	$ u_{\text{O}}^{\text{9}} $	$ {T_{10}}\left( {{M_{\text{E}}}} \right) $
	$ u_{\text{A}}^{\text{1}} $	$ {T_7}\left( {{M_{\text{A}}}} \right) $
	$ u_{\text{A}}^{\text{2}} $	$ {T_9}\left( {{M_{\text{A}}}} \right) $
	$ u_{\text{A}}^{\text{3}} $	$ {T_3}\left( {{M_{\text{A}}}} \right) \to {T_4}\left( {{M_{\text{A}}}} \right) \to {T_{10}}\left( {{M_{\text{A}}}} \right) \to {T_6}\left( {{M_{\text{A}}}} \right) $
	$ u_{\text{A}}^5 $	$ {T_5}\left( {{M_{\text{A}}}} \right) \to {T_1}\left( {{M_{\text{A}}}} \right) \to {T_{10}}\left( {{M_{\text{A}}}} \right) $
	$ u_{\text{A}}^6 $	$ {T_2}\left( {{M_{\text{A}}}} \right) \to {T_8}\left( {{M_{\text{A}}}} \right) $

Tab.2 Results of UAV task assignment

Fig.7 Results of UAV task assignment in instance 10×6

Fig.8 Task assignment results of deleting waiting times

Fig.9 Average fitness of different algorithms in hybrid strategy ablation experiments

Fig.10 Pareto frontiers of five particle swarm optimization algorithms in different instances

Fig.11 Average fitness of five particle swarm optimization algorithms in different instances

Tab.3 Average fitness decrease of proposed algorithm compared to comparison algorithms in three instances

Tab.4 Performance comparison of different particle swarm optimization algorithms in three instances

Tab.5 Running time of different particle swarm optimization algorithms in three instances

Fig.12 Running time of different algorithms in hybrid strategy ablation experiments


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