A Multi-Swarm Dynamic Crow Search Algorithm for Multi-UAV Dynamic Task Allocation

Efficient cooperative task allocation is essential for multiple unmanned aerial vehicles (UAVs) performing complex missions. However, diverse dynamic events in real-world scenarios require rapid response through dynamic task allocation (DTA). Although evolutionary algorithms have been widely adopted for DTA, existing methods often fail to maintain consistency between allocation decisions and actual operational states, consider only limited classes of dynamic events, and still leave room for performance improvement. This paper formulates multi-UAV DTA as a dynamic multi-objective optimization problem (DMOP) that jointly minimizes the residual target value and mission makespan, incorporating a state inheritance mechanism and a comprehensive set of dynamic events covering multiple facets of disruptions in observation task scenarios. To solve this DMOP, a multi-swarm dynamic crow search algorithm for task allocation (MDCSATA) is proposed, which integrates five strategies: violation-tolerant multi-swarm co-evolution for feasibility and diversity; objective-oriented heuristic initialization to accelerate convergence; an adaptive position update for better exploration and exploitation; stagnation and elite guided perturbation for intensified local exploitation; and an event-aware change response for rapid adaptation to dynamic events. Experiments on three constructed scenarios against seven state-of-the-art algorithms show that MDCSATA achieves superior performance on the evaluation metrics with acceptable runtime. It obtains the best MHV and MIGD in all scenarios, improving MHV by at least 0.93% and reducing MIGD by at least 12.92% across scenarios. These results confirm its effectiveness for DTA.