Heterogeneous multi-UAV collaborative task allocation based on modified chimp optimization algorithm

Aiming at the task assignment problem of heterogeneous UAV performing multi-type fixed tasks on the ground, a task allocation method based on Cooperative Multiple Task Allocation Problem (CMTAP) and Modified Chimp Optimization Algorithm (MChOA) is proposed. Firstly, the CMTAP model is established, and the combat scene is depicted by setting the elements in the quad, and a multi-objective evaluation function with the highest total return, the lowest threat cost, and the shortest total flight range is established. Secondly, the MChOA algorithm is used to complete the model solving: the chaotic reverse learning strategy is adopted to improve the diversity of the intial population distribution. In the iteration stage, the global and local search capabilities of the algorithm are better balanced by adopting the dynamic adjustment strategy of preemptive behavior. Comparative experiments show that compared with the original algorithm, the resulting task allocation scheme reduces the average total voyage in the two scale scenarios by 22.6% and 21.1%, respectively, while maintaining a higher task gain and a lower threat cost.