Dense unmanned aerial vehicle (UAV) networks comprising different types of UAVs have been widely applied across various domains, leading to potential conflicts within heterogeneous UAV networks. To ensure UAV safety, effective conflict resolution is crucial. However, existing learning-based methods are primarily designed for homogeneous UAVs and struggle to learn effective maneuvers for UAVs with varying attributes (like velocity and safety radius) or diverse motion pattern, such as hovering. To address these challenge, we propose a hierarchical framework with graph transformer-based reinforcement learning (HGT-RL) for conflict resolution in heterogeneous UAV networks. Specifically, a novel heterogeneous graph transformer network is employed to enhance the extraction of heterogeneous node information through orderly embeddings while incorporating a graph transformer network. To reduce the training complexity for heterogeneous UAVs, we generate the heuristic maneuvers at each time step, with the reinforcement learning model trained solely to refine these initial actions or perform emergency hovering. Experimental results demonstrate that HGT-RL outperforms previous methods in heterogeneous UAV networks, and we further validate its scalability across multiple scenarios.

Accurate forecasting of passenger flows is critical for maintaining the efficiency and resilience of airport operations. Recent advances in patch-based Transformer models have shown strong potential in various time series forecasting tasks. However, most existing methods rely on fixed-size patch embedding, making it difficult to model the complex and heterogeneous patterns of airport passenger flows. To address this issue, this paper proposes a deformable temporal–spectral transformer (named DTSFormer) that integrates a multiscale deformable partitioning module and a joint temporal–spectral filtering module. Specifically, the input sequence is dynamically partitioned into multiscale temporal patches via a novel window function-based masking, enabling the extraction of heterogeneous trends across different temporal stages. Then, within each scale, a frequency-domain attention mechanism is designed to capture both high- and low-frequency components, thereby emphasizing the volatility and periodicity inherent in airport passenger flows. Finally, the resulting multi-frequency features are subsequently fused in the time domain to jointly model short-term fluctuations and long-term trends. Comprehensive experiments are conducted on real-world passenger flow data collected at Beijing Capital International Airport from January 2023 to March 2024. The results indicate that the proposed method consistently outperforms state-of-the-art forecasting models across different prediction horizons. Further analysis shows that the deformable partitioning module aligns patch lengths with dominant periods and heterogeneous trends, enabling superior capture of sudden high-frequency fluctuations.

Effective conflict resolution is crucial to ensure the safety of unmanned aerial vehicles (UAVs) in increasingly congested low-altitude airspace. However, the inefficiency in representing large-scale UAV information hinders the performance of existing learning-based methods. To overcome this challenge, we propose an enhanced graph-based reinforcement learning (GRL) approach that models multi-agent interactions relationships. Specifically, a novel multi-scale graph reinforcement learning (MS-GRL) approach is utilized to learn UAV avoidance strategies in a dense environment. MS-GRL utilizes the time-evolving intensity of local conflicts to evaluate the global attention weights for UAVs in conflict, while aggregating UAV observation data through graph embedding and requisite feature refinement. In addition, to adaptively limit the safety region of the action space while minimize deviations from the original trajectory, a safety-constrained maneuver strategy is proposed. Experimental results demonstrate that MS-GRL outperforms state-of-the-art GRL methods when there are up to 100 UAVs and multiple obstacles.

Multi-aircraft task allocation (MATA) plays a vital role in improving mission efficiency under dynamic conditions. This paper proposes a novel coevolutionary genetic programming (CoGP) framework that automatically designs high-performance reactive heuristics for dynamic MATA problems. Unlike conventional single-tree genetic programming (GP) methods, CoGP jointly develops two interacting populations, i.e., task prioritizing heuristics and aircraft selection heuristics, to explicitly model the coupling between these two interdependent decision phases. A comprehensive terminal set is constructed to represent the dynamic states of aircraft and tasks, whereas a low-level heuristic template translates developed trees into executable allocation strategies. Extensive experiments on public benchmark instances simulating post-disaster emergency delivery demonstrate that CoGP achieves superior performance compared with state-of-the-art GP and heuristic methods, exhibiting strong adaptability, scalability, and real-time responsiveness in complex and dynamic rescue environments.

The Vehicle Routing Problem (VRP) is a classic optimization problem with diverse real-world applications. The neighborhood search has emerged as an effective approach, yielding high-quality solutions across different VRPs. However, most existing studies exhaustively explore all considered neighborhoods with a pre-fixed order, leading to an inefficient search process. To address this issue, this paper proposes a Learning-aided Neighborhood Search algorithm (LaNS) that employs a cutting-edge multi-agent reinforcement learning-driven adaptive operator/neighborhood selection mechanism to achieve efficient routing for VRP. Within this framework, two agents serve as high-level instructors, collaboratively guiding the search direction by selecting perturbation/improvement operators from a pool of low-level heuristics. Furthermore, to equip the agents with comprehensive information for learning guidance knowledge, we have developed a new informative state representation. This representation transforms the spatial route structures into an image-like tensor, allowing us to extract spatial features using a convolutional neural network. Comprehensive evaluations on diverse VRP benchmarks, including the capacitated VRP (CVRP), multi-depot VRP (MDVRP) and cumulative multi-depot VRP with energy constraints, demonstrate LaNS's superiority over the state-of-the-art neighborhood search methods as well as the existing learning-guided neighborhood search algorithms.

Dynamic air traffic flow management (DATFM) aims at flexibly balancing air traffic demand with limited airspace by scheduling aircraft, particularly during unforeseen events, to maintain efficiency in aviation operations. Genetic programming (GP) has shown success in evolving effective heuristics across various domains. However, directly adopting GP to DATFM may be less effective due to the computationally intense simulations required for large-scale aircraft decision-making over broad airspace. To address the issue, we develop a novel multifidelity surrogate-assisted GP. The core idea is that if a computationally efficient low-fidelity surrogate provides enough information to guide the population toward promising areas effectively, then employing more accurate but resource-intensive evaluations would only increase computational effort without enhancing the direction of evolution. A key innovation in our method is a surrogate management strategy that automatically determines when and which surrogate model to use, based on collective information from the evolving population. This approach allows for more effective management of computational resources during the evolutionary process, enabling exploration of a broader range of the heuristic space and increasing the likelihood of identifying promising solutions. The proposed method has been tested on various benchmark instances derived from actual air traffic data. Extensive experimental results demonstrate that the proposed algorithm significantly outperforms current state-of-the-art methods in both effectiveness and efficiency.

In recent years, unmanned aerial vehicles (UAVs) are considered to be integrated into wireless communication systems because of their tremendous advantages in mobility, cost, maneuverability, etc. In some real UAV-assisted communication scenarios, the dynamics of the environment, such as the roaming of served users, make it hard to obtain an optimal trajectory before the UAV is dispatched. Implanting an intelligent control policy into UAVs for distributed task execution is necessary to complete the task. In this paper, a UAV trajectory design problem is investigated for an orthorgonal-frequency-division-multiplexing (OFDM) wireless sensor network, which is dynamic because mobile sensors may randomly roam within a certain range. The UAV is expected to balance task efficiency with the safety constraint with a pre-trained onboard control policy. Compared to prior works, this work requires the policy to adapt to randomly generated obstacle maps, and also assumes that the UAV has no prior knowledge of the obstacles before it is dispatched, which brings about challenges to the problem. The motivation comes from adversarial environments without the specific obstacle distribution beforehand, such as a disaster area. The problem is formulated as a constrained Markov decision process (CMDP) model, which incorporates the safety constraint compared to basic MDP. Due to the assumption of randomized obstacle distribution and lack of prior knowledge, existing algorithms for CMDP can not be applied directly. To tackle this issue, we enhance reinforcement learning (RL) algorithm with a safety control mechanism to derive our novel safe reinforcement learning (Safe RL) algorithm, which is based on the framework of Lagrangian method. Compared to former algorithms about CMDP, our algorithm eliminates the premise that the safety model is known, the agent is able to learn safety judgement from scratch through its interactions with the environment. Simulation results demonstrate that our proposed algorithm outperforms the benchmark algorithm under the problem’s setup.

As a typical nonterrestrial network (NTN)-enabled Internet of Things (IoT), the multi-Unmanned aerial vehicle (UAV) collaborative surveillance network boasts efficient capabilities in information collection and transmission. However, manufacturing techniques and environmental conditions can lead to UAV failures, thereby impacting network performance. To recover the performance of the multi-UAV collaborative surveillance network, the effective movement of multiple UAVs is under investigation in order to improve target coverage and data backhaul efficiency. In this article, we present a novel multiagent deep reinforcement learning-based algorithm to accomplish network recovery. The proposed algorithm employs a multihead attention network to facilitate coupled multiobjective learning and overcome the limitations imposed by local information. Additionally, a stable learning method is introduced to address the difficult convergence problem caused by dynamic topology changes due to UAV motion. Experimental results show that the proposed algorithm can generate feasible multi-UAV motion strategies, effectively facilitating network recovery and improving the performance of the multi-UAV collaborative surveillance network in different scenarios.

Air traffic flow management plays a crucial role in efficient aviation. Most existing studies assume the flight speed as constant throughout the trip, leading to ineffective fixed-speed schedules. To address this issue, we propose a new problem model, which allows variable speed control to improve the flexibility and maneuverability of the management. In addition, we consider two conflicting objectives, which are minimizing the total flight delays and conflicts between flights, where the conflicts depend on the flight 4D trajectories (3D position plus time). To solve this new challenging problem, we propose a novel multi-objective evolutionary algorithm with new problem-specific individual representation and search operators. Specifically, the multi-chromosomes encoding scheme is designed to adapt to different types of operations. Then, to search the huge search space effectively, we develop a hybrid crossover operator that recombines the parents based on their flight routes. Furthermore, to balance the exploration and exploitation, we develop a new mutation strategy to utilize the heterogeneous search potential of different individuals. For exploitation, the knee individual in the Pareto front is improved by a new time shift operator for exploitation, and other non-dominated solutions are mutated by fixed-route mutation. For exploration, the dominated solutions are mutated randomly. To verify the effectiveness, we compare it with the real air traffic flow management schedules and the state-of-the-art algorithms on a range of real-world air traffic datasets. Extensive results show that the proposed algorithm can significantly outperform the baselines in generating safe and efficient 4D trajectories.

Air traffic flow management (ATFM) is the key driver of efficient aviation. It aims at balancing traffic demand against airspace capacity by scheduling aircraft, which is critical for air navigation service providers in delivering secure and sustainable air transport. Nowadays, the scale of scheduled aircraft grows dramatically along with the sharp increase in air traffic demand, which brings heavy pressure to efficient scheduling. Regarding safety and efficiency as two fundamental objectives of air transport, this paper proposes a cooperative co-evolutionary algorithm to solve large-scale multi-objective ATFM problems. First, a new multi-objective co-evolution framework with an evolving external archive is devised, in which the subcomponents collaborate with each other via the knee solution of the archive. Second, a novel fuzzy decomposition method is specifically designed to split the large-scale ATFM problem into small-size subcomponents by utilizing the spatiotemporal correlations of aircraft. During optimization, the proposed algorithm can continuously receive feedback from the optimization process and make the decomposition more likely better suited to the problem. Third, a new contribution-based probabilistic resource allocation mechanism is developed to automatically assign the computing resources to the unbalanced subcomponents. Finally, a test suite with different scales extracted from real air traffic data is created. Extensive experimental results show that, given the same number of fitness evaluations, the proposed algorithm significantly outperforms the state-of-the-art baselines in terms of effectiveness on all the benchmark instances.

Accurate airport traffic flow estimation is crucial for the secure and orderly operation of the aviation system. Recent advances in machine learning have achieved promising prediction results in the single-airport scenario. However, these works overlook the variational spatial interactions hidden among airports and show limited performances on the traffic flow prediction task for the aviation system which is composed of several airports. In this paper, we consider the multi-airport scenario and propose a novel spatio-temporal hybrid deep learning model to efficiently capture spatial correlations as well as temporal dependencies in a parallelized way. Specifically, we introduce the causal inference among airports to model their interactions and thus construct adaptive causality graphs in a data-driven manner to address the heterogeneity of airports. Furthermore, given that multi-source features are not applicable for all airports, a feature mask module is designated to adaptively select the features in spatial information mining. Extensive experiments are conducted on the real data of top-30 busiest airports in China. The results show that our spatio-temporal deep learning approach is superior to state-of-the-art methodologies and the improvement ratio is up to 4.7% against benchmarks. Ablation studies emphasize the power of the proposed adaptive causality graph and the feature mask module. All of these prove the effectiveness of the proposed methodology.

Accurate flight delay prediction is crucial for the secure and effective operation of the air traffic system. Recent advances in modeling inter-airport relationships present a promising approach for investigating flight delay prediction from the multi-airport scenario. However, the previous prediction works only accounted for the simplistic relationships such as traffic flow or geographical distance, overlooking the intricate interactions among airports and thus proving inadequate. In this paper, we leverage casual inference to precisely model inter-airport relationships and propose a self-corrective spatio-temporal graph neural network (named CausalNet) for flight delay prediction. Specifically, Granger causality inference coupled with a self-correction module is designed to construct causality graphs among airports and dynamically modify them based on the current airport’s delays. Additionally, the features of the causality graphs are adaptively extracted and utilized to address the heterogeneity of airports. Extensive experiments are conducted on the real data of top-74 busiest airports in China. The results show that CausalNet is superior to baselines. Ablation studies emphasize the power of the proposed self-correction causality graph and the graph feature extraction module. All of these prove the effectiveness of the proposed methodology.

With technological breakthroughs, drone deliveries have become increasingly popular, especially during the COVID-19 pandemic. Driven by both economical benefit and efficiency, drone-truck combined deliveries are in demand. However, it is very challenging to handle the collaboration between trucks and drones. Existing methods for truck-only routing cannot be directly applied, since their solution representations and search operators cannot consider the drone-truck collaborations effectively. In this article, we model the system as traveling salesman problem with drones (TSP-Ds), and propose a new Memetic algorithm named MATSP-D for solving it. Specifically, we design a new drone-truck solution representation and develop new crossover and local search operators under the new representation, which can modify the drone services effectively. MATSP-D conducts exploration by crossover, and exploitation by a variable neighborhood search process. The experimental results show that the proposed MATSP-D significantly outperforms the state-of-the-art algorithms for most test instances, especially the large instances with more complex collaborations between the truck and drone. Further analysis verifies the effectiveness of the newly developed local search operators in searching for better-drone-truck collaborations.

Since the unmanned aerial vehicle (UAV) network and intelligent reflecting surface (IRS) technology can flexibly change wireless network links signal, the UAV-IRS network system is a potential solution to increase the communication performance gain. Motivated by the practicality of UAV-IRS networks, a non-orthogonal multiple access (NOMA) heterogeneous UAV communication system with simultaneous wireless information and power transfer (SWIPT) is considered, which consists of multiple UAV base stations (UBSs), a macro base station (MBS), and multiple IRSs for auxiliary communications. This paper pursues a goal to receive the system energy efficiency (EE) maximization by resource allocation and reflecting design of IRSs. Due to the strong coupling among multiple parameters in the original problem, this complex non-convex problem is decomposed into three stages. In the first stage, this paper decouples the problem into two subproblems of NOMA subchannel assignment and SIC decoding order to find the optimal solution separately. For the second stage, under the constraints of UAV’s maximum transmit power, users’ quality of service (QoS) requirements, user energy harvesting threshold and cross-layer interference constraints, a beamforming design based on Lagrangian duality is exploited. For the third stage, the power splitting (PS) factors and the reflecting phases of the IRS are jointly optimized using the penalty-SDR algorithm to approximate the suboptimal solution. Finally, the simulation curves exhibit the validity and excellent performance of the co-design scheme in improving the system EE.

A set of low-cost sensors is deployed to infer the network topology of a self-organizing wireless network. The sensors operate in a non-invasive fashion, extracting only the timings of data packets and acknowledgment (ACK) packets from all nodes in a network. The meta-data also reports the source node of each packet, but not the destination nodes or the contents of the packets. A central processor collects the meta-data from the sensors, and the goal is for the processor to infer the network topology based solely on such information. Prior work leveraged causality metrics to identify which links are active. If the data timings and ACK timings of two nodes– say node 1 and node 2, respectively– are causally related, this may be taken as evidence that node 1 is communicating to node 2 (which sends back ACK packets to node 1). This paper starts with the observation that packet losses can weaken the causality relationship between data and ACK timing streams. To obviate this problem, a new Expectation Maximization (EM)-based algorithm is introduced– EM-causality discovery algorithm (EM-CDA)– which treats packet losses as latent variables. EM-CDA iterates between the estimation of packet losses and the evaluation of causality metrics. The method is validated through extensive experiments in wireless sensor networks on the NS-3 simulation platform.

Accurate airport capacity estimation is crucial for the secure and orderly operation of the aviation system. However, such estimation is a non-trivial task as capacity depends on various meteorological and operational features. The complex coupling characteristics among these multi-source features have proved to be challenging for most of the traditional regression models. Recently, enhanced by its excellent ability to mine nonlinear relationships, the machine learning methods trigger widely applications. However, due to the imbalance of features scatter and the neglect of temporal dependences in aviation systems, existing machine learning methods for airport capacity prediction still have room for improvement. In light of these, this paper presents a novel airport capacity prediction method based on the multi-channel fusion Transformer model (MF-Transformer). Besides the commonly used aviation features, we unprecedentedly harness the power of the high-dimensional meteorological feature for accurate prediction. As to the model, we construct a multi-channel feature fusion structure, which includes a three-channel network for multi-source features extraction and an attention-based feature fusion module between channels. In each channel, the Transformer-based model is utilized to capture the temporal dependences of features. We conduct experiments on the capacity prediction tasks of the Beijing Capital International Airport which is the largest airport in China and verify that the proposed MF-Transformer outperforms benchmarks under different prediction horizons.

Airspace complexity is a critical metric in current Air Traffic Management systems for indicating the security degree of airspace operations. Airspace complexity can be affected by many coupling factors in a complicated and nonlinear way, making it extremely difficult to be evaluated. In recent years, machine learning has been proved as a promising approach and achieved significant results in evaluating airspace complexity. However, existing machine learning based approaches require a large number of airspace operational data labeled by experts. Due to the high cost in labeling the operational data and the dynamical nature of the airspace operating environment, such data are often limited and may not be suitable for the changing airspace situation. In light of these, we propose a novel unsupervised learning approach for airspace complexity evaluation based on a deep neural network trained by unlabeled samples. We introduce a new loss function to better address the characteristics pertaining to airspace complexity data, including dimension coupling, category imbalance, and overlapped boundaries. Due to these characteristics, the generalization ability of existing unsupervised models is adversely impacted. The proposed approach is validated through extensive experiments based on the real-world data of six sectors in Southwestern China airspace. Experimental results show that our deep unsupervised model outperforms the state-of-the-art methods in terms of airspace complexity evaluation accuracy.

As revolutionary technologies that can actively change the communication link signal, intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) have emerged as reliable, economical and convenient wireless communication solutions for a variety of practical scenarios. Therefore, this paper focuses on an IRS empowered UAV downlink communication network, where the dynamic UAV establishes a cascade link via IRS to provide signal enhancement services for multiple users. Considering constraints of transmit power, flight speed and area at the UAV and the reflecting constraints at the IRS, the block coordinate descent (BCD) method based on resource allocation, reflecting design and trajectory optimization is adopted to maximize the sum-rate of all users. The proposed problem is converted by using quadratic transformation and Lagrangian dual transformation. Then applying for the approximate linear method and Iterative Rank Minimization (IRM) to optimize the transmit power of UAV and phase shift of IRS respectively. Since additional reflection propagation paths by IRS, the complexity of the channel model makes the trajectory design difficult. To tackle this problem, this paper proposes a UAV trajectory optimization method based on enhanced reinforcement learning with the fixed initial location and destination. In the end, the convergence of the proposed scheme is effectively verified by simulations. Moreover, abundant simulation comparisons between the proposed scheme and other benchmark schemes demonstrate the validity and high performance gains of the proposed algorithm.

In this paper, we are concerned with exploring the theoretically and technically research outcomes for the conflict resolution (CR) of multiple unmanned aerial vehicles (UAVs) by using the Internet of Things technologies. We propose a satisficing algorithm to mitigate the CR problem of multiple UAVs. Specifically, we first formulate the CR problem as a game model and design strategies of the game model based on flight characteristics of UAVs. Next, a satisficing game theory is used to mitigate the formulated problem. Furthermore, required time of arrival, which is a new judgment parameter of the strategy utility, is developed to ensure that the whole system can reach a socially acceptable compromise. Simulation results verify the effectiveness and adaptability of the proposed algorithm under complex environments.

Air traffic flow management (ATFM) aims to facilitate the utilization of airspace and airport resources and is critical in air transportation systems. During the past decades, several challenging problems have arisen from this domain and attracted intensive studies. This paper addresses the problem of alleviating the airspace congestion and reducing the flight delays in ATFM simultaneously. We formulate this problem as a multi-objective air traffic network flow optimization (MATNFO) problem. In this MATNFO model, comprehensive ATFM actions, for instance, ground-holding, airborne-holding, rerouting, and speed control, are considered. Meanwhile, a systematic approach, namely route and time-slot assignment (RTA) algorithm, is developed to solve the MATNFO problem. The idea of divide-and-conquer is embedded in the algorithm by sequentially applying both route searching module and time refinement module. Furthermore, for the sake of efficiency, a pre-selection operator is proposed as one heuristic strategy to identify promising solutions and reduce the search space by defining a sector equilibrium metric. Experiments on real data of the Chinese airspace show that the RTA algorithm outperforms an existing competitor and three related multi-objective evolutionary algorithms. In addition, RTA is competent for high-quality real-time air traffic network flow assignment.