基于改进黏菌算法的异构无人机任务分配算法

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大连海事大学学报 ›› 2026, Vol. 52 ›› Issue (1) : 111-122.

刘文¹，那振宇^*1,2，李梦悦¹，庞贵梅¹，张金波^2,3

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Heterogeneous UAVs task allocation algorithm based on improved slime mold algorithm

LIU Wen¹，NA Zhenyu^*1,2，LI Mengyue¹, PANG Guimei¹，ZHANG Jinbo^2,3#br#
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摘要

针对异构无人机在多样化任务场景中的任务分配问题，提出一种基于改进黏菌算法的异构无人机群任务分配算法。首先，从任务时效性、性能参数等角度出发，在现有多参数建模框架基础上，进一步完善任务多样性与无人机异构性的参数刻画，使其更适配本文的多类型任务与多约束场景。其次，针对黏菌算法收敛速度慢、求解精度低等问题，引入Tent混沌映射、线性选择搜索范围、[JP2]改变个体更新方式与最优邻域扰动策略进行优化。最后，将改进黏菌算法与任务分配模型相结合，通过设置不同的任务场景规模验证了所提算法的有效性。仿真结果表明，在不同规模的任务场景下，所提算法可以最小的任务代价实现任务的合理分配；相较于基准方案，所提算法在两种不同规模任务场景中的综合目标函数分别至少降低了10.6%和15.4%。

Abstract

To address the task allocation problem of heterogeneous unmanned aerial vehicles (UAVs) in diverse mission scenarios, a task allocation algorithm for heterogeneous UAV swarms based on the improved slime mold algorithm (SMA) was proposed. Firstly, from the perspectives of task timeliness and performance parameters, and based on the existing multiparameter modeling framework, this paper further improved the parameter characterization of task diversity and UAV heterogeneity to better adapt to the multitype tasks and multiconstraint scenarios. Secondly, to address the problems of slow convergence speed and low solution accuracy of the SMA, optimization was carried out by introducing Tent chaotic mapping, linear dynamic search range, improved individual update mechanism and optimal neighborhood perturbation strategy. Finally, the improved SMA was integrated with the proposed task allocation model, and its effectiveness was verified by setting task scenarios with different scales. Simulation results show that the proposed algorithm can achieve reasonable task allocation with minimal task cost under different scenario scales. Compared with benchmark methods, the proposed algorithm reduces the comprehensive objective function by at least 10.6% and 15.4% in two different task scales, respectively.

导出引用

刘文, 那振宇, 李梦悦, 庞贵梅, 张金波. 基于改进黏菌算法的异构无人机任务分配算法[J]. 大连海事大学学报. 2026, 52(1): 111-122

LIU Wen, NA Zhenyu, LI Mengyue, PANG Guimei, ZHANG Jinbo. Heterogeneous UAVs task allocation algorithm based on improved slime mold algorithm[J]. Journal of Dalian Maritime University. 2026, 52(1): 111-122

参考文献

［1］JIANG Z L, SONG T T, YANG B W, et al. Fault-tolerant control for multi-UAV exploration system via reinforcement learning algorithm［J］. Aerospace, 2024, 11(5): 372.

［2］JU C Y, SON H I. Multiple UAV systems for agricultural applications: control, implementation, and evaluation［J］. Electronics, 2018, 7(9): 162.

［3］赵慧敏, 罗贺, 阴酉龙, 等. 面向集合任务的多无人机电力巡检任务分配方法研究［J］. 系统工程理论与实践, 2025, 45(2): 666-684.

ZHAO H M, LUO H, YIN Y L, et al. Research on task allocation method for multipledrones power inspection for collective tasks［J］. Systems Engineering - Theory & Practice, 2025, 45(2): 666-684.(in Chinese)

［4］ZHANG Z, JIANG J, XUH Y. Distributed dynamic task allocation for unmanned aerial vehicle swarm systems: a networked evolutionary game-theoretic approach［J］. Chinese Journal of Aeronautics, 2024, 37(6): 182-204.

［5］LI K, YAN X X, HAN Y. Multi-mechanism swarm optimization for multi-UAV task assignment and path planning in transmission line inspection under multi-wind field［J］. Applied Soft Computing, 2023, 150: 111033.

［6］WANG Z L, WANG B, WEI Y L, et al. Cooperative multi-task assignment of multiple UAVs with improved genetic algorithm based on beetle antennae search［C］//2020 39th Chinese Control Conference (CCC). Shenyang: IEEE, 2020: 1605-1610.doi:10.23919/CCC50068.2020.9189661.

［7］SHIMA T, RASMUSSEN S J, SPARKS A G, et al. Multiple task assignments for cooperating uninhabited aerial vehicles using genetic algorithms［J］. Computers & Operations Research, 2006, 33(11): 3252-3269.

［8］EDISON E, SHIMA T. Integrated task assignment and path optimization for cooperating uninhabited aerial vehicles using genetic algorithms［J］. Computers & Operations Research, 2011, 38(1): 340-356.

［9］XIE S L, ZHANG A, BI W H, et al. Multi-UAV task allocation under constraint［J］. Applied Sciences, 2019, 9(11): 2184.

［10］GAO X H, WANG L, YU X Y, et al. Conditional probability based multi-objective cooperative task assignment for heterogeneous UAVs［J］. Engineering Applications of Artificial Intelligence, 2023, 123: 106404.

［11］ZHANG R, CHEN X, LI M Y, et al. Multiple UAVs task assignment based on improved dung beetle optimizer［C］//2023 IEEE International Conference on Unmanned Systems (ICUS).［S.l.］: IEEE, 2023: 779-785. doi:10.1109/ICUS58632.2023.10318257.

［12］高谦, 张玉良, 曹艳. 多无人机协同作战中的任务分配与路径规划算法研究［J］. 无线互联科技, 2024, 21(18): 23-26.

GAO Q, ZHANG Y L, CAO Y. Research on task allocation and path planning algorithms in multi-UAV cooperative combat［J］. Wireless Internet Science and Technology, 2024, 21(18): 23-26.(in Chinese)

［13］YAN S K. Research on heterogeneous UAVs task assignment based on improved contract net algorithm［C］//2021 6th International Conference on Robotics and Automation Engineering (ICRAE). Guangzhou : IEEE, 2021: 60-64. doi: 10.1109/ICRAE53653.2021.9657799.

［14］ZHANG K W, ZHAO X L, LI Z Z, et al. Real-time reconnaissance task assignment of multi-UAV based on improved contract network［C］// 2020 International Conference on Artificial Intelligence and Computer Engineering (ICAICE) . Beijing :IEEE, 2020 : 472-479. doi: 10.1109/ICAICE51518.2020.00098.

［15］LI J X, YANG X R, YANG Y J, et al. Cooperative mapping task assignment of heterogeneous multi-UAV using an improved genetic algorithm［J］. Knowledge-Based Systems, 2024, 296: 111830.

［16］YAN F, CHU J, HU J W, et al. Cooperative task allocation with simultaneous arrival and resource constraint for multi-UAV using a genetic algorithm［J］. Expert Systems with Applications, 2024, 245: 123023.

［17］WANG Z, LIU L, LONG T, et al. Multi-UAV reconnaissance task allocation for heterogeneous targets using an opposition-based genetic algorithm with double-chromosome encoding［J］. Chinese Journal of Aeronautics, 2018, 31(2): 339-350.

［18］WANG Z H, ZHANG J L. A task allocation algorithm for a swarm of unmanned aerial vehicles based on bionic wolf pack method［J］. Knowledge-Based Systems, 2022, 250: 109072.

［19］LI S M, CHEN H L, WANG M J, et al. Slime mould algorithm: a new method for stochastic optimization［J］. Future Generation Computer Systems, 2020, 111: 300-323.

［20］ALTAY O. Chaotic slime mould optimization algorithm for global optimization［J］. Artificial Intelligence Review, 2022, 55: 3979-4040.

［21］CHEN H, LI X B, LI S L, et al. Improved slime mould algorithm hybridizing chaotic maps and differential evolution strategy for global optimization［J］. IEEE Access, 2022, 10: 66811-66830.

［22］XIONG H, GE B L, LIU J Z. An improved slime mold algorithm for cooperative path planning of multi-UAVs*［C］//2023 IEEE International Conference on Robotics and Biomimetics (ROBIO). Koh Samui: IEEE, 2023. doi: 10.1109/ROBIO58561.2023.10354937.

［23］KENNEDY J, EBERHART R. Particle swarm optimization［C］//Proceedings of ICNN’95 International Conference on Neural Networks. Piscataway: IEEE, 1995: 1942-1948.

［24］MIRJALILI S, LEWIS A. Grey wolf optimizer［J］. Advances in Engineering Software, 2014, 69: 46-61.

［25］WANG J, WANG W C, HU X X, et al. Black-winged kite algorithm: a nature inspired meta heuristic for solving benchmark functions and engineering problems［J］. Artificial Intelligence Review, 2024, 57: 98.