Energy learning hyper-heuristic algorithm for cooperative task assignment of heterogeneous UAVs under complex constraints
Mengshun Yuan, Mou Chen, Tongle Zhou, Zengliang Han · Defence Technology · 2025
Cooperative task assignment is one of the key research focuses in the field of unmanned aerial vehicles (UAVs). In this paper, an energy learning hyper-heuristic (EL-HH) algorithm is proposed to address the cooperative task assignment problem of heterogeneous UAVs under complex constraints. First, a mathematical model is designed to define the scenario, complex constraints, and objective function of the problem. Then, the scheme encoding, the EL-HH strategy, multiple optimization operators, and the task sequence and time adjustment strategies are designed in the EL-HH algorithm. The scheme encoding is designed with three layers: task sequence, UAV sequence, and waiting time. The EL-HH strategy applies an energy learning method to adaptively adjust the energies of operators, thereby facilitating the selection and application of operators. Multiple optimization operators can update schemes in different ways, enabling the algorithm to fully explore the solution space. Afterward, the task order and time adjustment strategies are designed to adjust task order and insert waiting time. Through the iterative optimization process, a satisfactory assignment scheme is ultimately produced. Finally, simulation and experiment verify the effectiveness of the proposed algorithm.