With the rapid proliferation of non-geostationary orbit (NGSO) mega-constellations, beam hopping (BH) has become indispensable for resource scheduling in multi-satellite, multi-coverage scenarios. By dynamically adjusting spot beam power and pointing within each time slot, BH enables highly efficient spectrum utilization. A principal engineering challenge is the real-time generation of beam hopping time plans (BHTP). Traditional algorithms, such as the round-robin strategy, distribute beams evenly across all service cells in a round-robin fashion. However, real traffic follows a long-tail distribution; the most active 10% of hotspot cells generate more than 50% of the aggregate demand, making uniform allocation inadequate. To address this issue, existing frameworks adopt a genetic algorithm (GA), whose throughput is approximately 80.7% higher than the traditional baseline. Operational satellite footprints encompass more than 1,000 service cells. The GA requires 67.8 s to generate a BHTP for 1,127 cells. With a 550 km LEO satellite providing only a 300 s visibility window, multiple online recomputations are impractical. State-of-the-art algorithms, such as multi-agent deep reinforcement learning (MADRL), fail to converge once the cell count exceeds 200. To overcome these challenges, we propose a novel BH scheduling algorithm Aidos. The algorithm integrates traffic-aware random-key encoding into a multi-objective metaheuristic search, and then applies a sliding-window Beta resampling strategy during adaptive distribution evolution, to improve both the search efficiency and the solution quality of the BHTP. Experiments demonstrate that Aidos improves throughput by 79.2% and reduces latency by 99.45%. Its average computation time is 9.3 s, enabling online replanning within a 300 s satellite overpass window.

翻译：随着非静止轨道（NGSO）巨型星座的快速普及，波束跳变（BH）已成为多星多覆盖场景下资源调度的关键技术。通过在每个时隙内动态调整点波束功率与指向，BH可实现极高的频谱利用效率。一个主要的工程挑战是实时生成波束跳变时间计划（BHTP）。传统算法（如循环调度策略）以轮询方式将波束均匀分配给所有服务小区。然而，实际流量呈现长尾分布：最活跃的10%热点小区产生超过50%的总需求，这使得均匀分配方案无法满足需求。为此，现有框架采用遗传算法（GA），其吞吐量较传统基线提升约80.7%。在包含超过1000个服务小区的运行卫星覆盖范围内，GA生成针对1127个小区的BHTP需耗时67.8秒。由于550 km低轨卫星仅提供300秒可见窗口，多次在线重计算并不现实。最先进的算法（如多智能体深度强化学习MADRL）在小区数超过200时无法收敛。为克服上述挑战，我们提出一种新型BH调度算法Aidos。该算法将流量感知随机密钥编码融入多目标元启发式搜索，并在自适应分布进化过程中采用滑动窗口Beta重采样策略，以提升BHTP的搜索效率与解质量。实验表明，Aidos使吞吐量提升79.2%，延迟降低99.45%，平均计算时间为9.3秒，从而支持在300秒卫星过境窗口内实现在线重规划。