Addressing the challenges of inefficient dynamic programming and unstable performance in heterogeneous unmanned aerial vehicle (UAV) systems for tracking mobile targets in complex environments, this paper establishes a task allocation optimization model that comprehensively incorporates the impact of UAV capture range, flight distance, and mission time benefits on allocation results. Meanwhile, specific enhancements to the classical Kalman filter are introduced to mitigate allocation oscillations during target pursuit, thereby improving model robustness. To solve this model, we propose a Gray Wolf-Hippopotamus Optimization Algorithm (GWHO) for real-time allocation. In addition, for newly emerging targets, an event-triggered dominant-solution retention strategy is implemented to preserve high-quality solutions from historical allocations and accelerate algorithm convergence. Simulation analyses demonstrate that the improved algorithm achieves a 25.8% faster convergence on benchmark test functions while reducing allocation oscillations by over 60%. Moreover, the model consistently achieves optimal allocation results across diverse scales of UAV swarms and targets, validating its effectiveness and applicability.
Wu et al. (Fri,) studied this question.
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