Emergency communication networks play a crucial role in disaster relief operations. Current automated deployment strategies based on rule-driven or heuristic algorithms struggle to adapt to the dynamic and heterogeneous network environments in disaster scenarios, while manual command deployment is constrained by personnel expertise and response time requirements, leading to suboptimal trade-offs between deployment efficiency and reliability. To address these challenges, this study proposes a novel deep reinforcement learning framework with a fully convolutional value network architecture, which achieves breakthroughs in multi-dimensional spatial decision-making through end-to-end feature extraction. This design effectively mitigates the “curse of dimensionality” inherent in traditional reinforcement learning methods for topology planning. Experimental results demonstrate that the proposed method effectively accomplishes the planning tasks of emergency communication hub elements, significantly improving deployment efficiency while maintaining robustness in complex environments.

The growing affordability, autonomy, and swarming of small unmanned aerial vehicles (UAVs) turn low-altitude defense from single-shot interception into a multi-node cooperative decision problem, in which the loss of sensing, coordination, or engagement nodes breaks the closed loops linking them. This study formulates their recovery as the dynamic reconfiguration of cooperative counter-UAV task chains. Given a pre-disturbance plan and a set of failed defending nodes, reconfiguration is modeled as a constrained bi-objective optimization balancing recovered engagement effectiveness against the change to the baseline plan, and is solved by Multi-Agent Heuristic Evolution (MAHE), an automated heuristic design framework whose evolution, coordinator, repair, and reflection agents—driven by a large language model—evolve scoring heuristics for a fixed reconfiguration solver. Across instances of varying scale and under light-to-heavy node loss, MAHE outperforms both a single-agent heuristic-design counterpart and a range of hand-crafted solvers: the latter lose most of their solution quality as the problem grows, whereas MAHE preserves it and sustains high recovery at a nearly constant reconfiguration cost; an ablation confirms that its agents contribute complementary gains. These results indicate that automatically generated, reconfiguration-specific heuristics provide a scalable route to dynamic, heterogeneous, and constraint-intensive counter-UAV task planning.

Accurate long-term forecasting of Quality of Transmission (QoT) is critical for the proactive operation and condition-aware management of dynamic elastic optical networks. However, the evolution of QoT is governed by multi-scale dynamics, including slow equipment aging, periodic operating variations, and short-term channel fluctuations, which a single temporal model struggles to capture jointly. To address this issue, we propose PA-TCN-Informer, a decomposition-driven parallel forecasting framework for long-horizon QoT prediction. The proposed framework first applies Seasonal-Trend decomposition using Loess (STL) to separate the Q-factor sequence into trend, seasonal, and residual components, and then employs Variational Mode Decomposition (VMD) to further resolve the residual into short-term fluctuation modes. The decomposed components, together with physical-layer monitoring features, are fed into a parallel TCN–Informer architecture, in which the TCN branch captures local temporal patterns while the Informer branch models long-range dependencies; the two streams are subsequently fused. We evaluate the proposed framework through Optuna-based hyperparameter optimization, STL/VMD sensitivity analysis, decomposition-method comparison, multi-seed baseline comparison with statistical testing, and zero-shot leave-one-dataset-out cross-domain evaluation. On the primary dataset, PA-TCN-Informer achieves the best overall forecasting accuracy among the compared models and reduces MAE by 2.2% relative to the serial TCN–Informer. In addition, the staged STL-VMD preprocessing alone yields a 60.8% reduction in MAE compared with raw inputs, confirming the value of physically interpretable multi-scale decomposition. In the zero-shot cross-domain setting, PA-TCN-Informer remains competitive across target domains. These results demonstrate that the proposed framework provides an effective and interpretable approach to QoT forecasting, and they further indicate that topology-aware modeling is a promising direction for improving cross-domain generalization.

The growing affordability, autonomy, and swarming of small unmanned aerial vehicles (UAVs) turn low-altitude defense from single-shot interception into a multi-node cooperative decision problem, in which the loss of sensing, coordination, or engagement nodes breaks the closed loops linking them. This study formulates their recovery as the dynamic reconfiguration of cooperative counter-UAV task chains. Given a pre-disturbance plan and a set of failed defending nodes, reconfiguration is modeled as a constrained bi-objective optimization balancing recovered engagement effectiveness against the change to the baseline plan and is solved by Multi-Agent Heuristic Evolution (MAHE), an automated heuristic design framework whose evolution, coordinator, repair, and reflection agents—driven by a large language model—evolve scoring heuristics for a fixed reconfiguration solver. Across instances of varying scale and under light-to-heavy node loss conditions, MAHE outperforms both a single-agent heuristic design counterpart and a range of hand-crafted solvers: on ten held-out test instances spanning 8–320 targets it attains the highest overall normalized hypervolume (0.947, versus 0.935 for the single-agent counterpart and 0.30–0.45 for the hand-crafted solvers) and the best mean rank (1.43 of six methods, $p<{10}^{-5}$); the hand-crafted solvers lose most of their solution quality as the problem grows, whereas MAHE preserves it and sustains high recovery at a nearly constant reconfiguration cost. An ablation confirms that its agents contribute complementary gains. These simulation results indicate that automatically generated, reconfiguration-specific heuristics offer a scalable algorithmic foundation for dynamic, heterogeneous, and constraint-intensive counter-UAV task-chain reconfiguration.