GeoHopNet: Hopfield-Augmented Sparse Spatial Attention for Dynamic UAV Site Location Problem
This addresses computational bottlenecks in urban UAV site selection, offering a scalable solution for real-world applications, though it is incremental as it builds on existing attention and Hopfield network methods.
The paper tackles the dynamic UAV site location problem by proposing GeoHopNet, a Hopfield-augmented sparse spatial attention network, which achieves high-quality solutions (0.22% optimality gap) for large-scale instances with 1,000 nodes in under 0.1 seconds, improving solution quality by 22.2% and speed by 1.8× compared to baselines.
The rapid development of urban low-altitude unmanned aerial vehicle (UAV) economy poses new challenges for dynamic site selection of UAV landing points and supply stations. Traditional deep reinforcement learning methods face computational complexity bottlenecks, particularly with standard attention mechanisms, when handling large-scale urban-level location problems. This paper proposes GeoHopNet, a Hopfield-augmented sparse spatial attention network specifically designed for dynamic UAV site location problems. Our approach introduces four core innovations: (1) distance-biased multi-head attention mechanism that explicitly encodes spatial geometric information; (2) K-nearest neighbor sparse attention that reduces computational complexity from $O(N^2)$ to $O(NK)$; (3) a modern Hopfield external memory module; and (4) a memory regularization strategy. Experimental results demonstrate that GeoHopNet extends the boundary of solvable problem sizes. For large-scale instances with 1,000 nodes, where standard attention models become prohibitively slow (over 3 seconds per instance) and traditional solvers fail, GeoHopNet finds high-quality solutions (0.22\% optimality gap) in under 0.1 seconds. Compared to the state-of-the-art ADNet baseline on 100-node instances, our method improves solution quality by 22.2\% and is 1.8$\times$ faster.