Heterogeneous Multi-UAV Task Allocation Based on Improved Multi-Objective Grey Wolf Optimization Algorithm

With the wide application of UAVs, the scenarios have become more complex, and UAV tasking is facing more requirements. First of all, a heterogeneous multi-UAV collaborative multitasking assignment model is constructed, which includes multiple practical constraints such as UAV type, mission type, and timing of mission execution. Then, the optimization objective which includes minimizing total flight distance and mission completion time is set up. In addition, traditional multi-objective optimization algorithms cannot effectively search the problem space and generate feasible solutions. In order to solve the model efficiently, an improved multi-objective gray wolf optimization algorithm is proposed in this article. The diversity of the population was increased by a hierarchical coding approach and an improved initialization strategy. A competency-based nonlinear parametric control strategy is proposed to improve the convergence of the algorithm. Furthermore, a learning update strategy based on external archive sets is designed to enhance the search efficiency and further increase the convergence of the algorithm. Finally, it is demonstrated that the improved algorithm can effectively solve the heterogeneous multi-UAV task allocation problem by comparison simulations.