TY - JOUR
T1 - An adaptive memetic algorithm for multi-UAV cooperative task assignment under complex constraints
AU - Meng, Kai
AU - Jiang, Zonghong
AU - Xin, Bin
AU - Deng, Fang
AU - Chen, Chen
N1 - Publisher Copyright:
© 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
PY - 2026/5
Y1 - 2026/5
N2 - Multiple unmanned aerial vehicle (multi-UAV) cooperative task assignment is a critical technology for large-scale reconnaissance and strike operations. In realistic combat environments, this problem is subject to complex constraints, including task precedence relations, simultaneous arrival requirements, task-specific resource demands, per-UAV resource capacity limits, and capability-requirement matching. However, most existing studies consider only subsets of these constraints or oversimplify their interactions, often resulting in infeasible or suboptimal task assignment solutions. To address these limitations, we formulate a mixed-variable multi-UAV cooperative task assignment model that incorporates all the aforementioned constraints and optimizes a weighted objective function comprising total flight distance, maximum task completion time, and a cost-effectiveness ratio. To efficiently solve the proposed model, we develop an adaptive memetic algorithm (AMA). Specifically, a target-bundled encoding scheme combined with a constraint-aware initialization strategy is introduced to generate high-quality feasible initial solutions. To enhance global exploration capability, a module-based crossover operator strategy and a sparrow-search-inspired mutation operator strategy are designed to effectively increase population diversity. Promising solutions are subsequently refined using knowledge-specific local search operators, with an adaptive operator selection mechanism dynamically choosing appropriate operators and a dual-space adaptive frequency control strategy determining when to activate local search based on feedback from both the decision and objective spaces. Experimental results on 100 test instances with varying numbers of UAVs and tasks demonstrate that AMA consistently outperforms three prevailing methods, achieving a 65%–92% reduction in average relative percentage deviation, thereby confirming its effectiveness and robustness.
AB - Multiple unmanned aerial vehicle (multi-UAV) cooperative task assignment is a critical technology for large-scale reconnaissance and strike operations. In realistic combat environments, this problem is subject to complex constraints, including task precedence relations, simultaneous arrival requirements, task-specific resource demands, per-UAV resource capacity limits, and capability-requirement matching. However, most existing studies consider only subsets of these constraints or oversimplify their interactions, often resulting in infeasible or suboptimal task assignment solutions. To address these limitations, we formulate a mixed-variable multi-UAV cooperative task assignment model that incorporates all the aforementioned constraints and optimizes a weighted objective function comprising total flight distance, maximum task completion time, and a cost-effectiveness ratio. To efficiently solve the proposed model, we develop an adaptive memetic algorithm (AMA). Specifically, a target-bundled encoding scheme combined with a constraint-aware initialization strategy is introduced to generate high-quality feasible initial solutions. To enhance global exploration capability, a module-based crossover operator strategy and a sparrow-search-inspired mutation operator strategy are designed to effectively increase population diversity. Promising solutions are subsequently refined using knowledge-specific local search operators, with an adaptive operator selection mechanism dynamically choosing appropriate operators and a dual-space adaptive frequency control strategy determining when to activate local search based on feedback from both the decision and objective spaces. Experimental results on 100 test instances with varying numbers of UAVs and tasks demonstrate that AMA consistently outperforms three prevailing methods, achieving a 65%–92% reduction in average relative percentage deviation, thereby confirming its effectiveness and robustness.
KW - Cooperative task assignment
KW - Memetic algorithm
KW - Mixed-variable optimization
KW - Unmanned aerial vehicles
UR - https://www.scopus.com/pages/publications/105036234593
U2 - 10.1016/j.swevo.2026.102395
DO - 10.1016/j.swevo.2026.102395
M3 - Article
AN - SCOPUS:105036234593
SN - 2210-6502
VL - 105
JO - Swarm and Evolutionary Computation
JF - Swarm and Evolutionary Computation
M1 - 102395
ER -