Unmanned aerial vehicle (UAV) swarms have shown substantial potential to enhance operational efficiency and reduce strike costs, presenting extensive applications in modern urban warfare. However, achieving effective strike performance in complex urban environments remains challenging, particularly when considering three-dimensional obstacles and threat zones simultaneously, which can significantly degrade strike effectiveness. To address this challenge, this paper proposes a target strike strategy using the Electric Eel Foraging Optimization (EEFO) algorithm, a heuristic optimization method designed to ensure precise strikes in complex environments. The problem is formulated with specific constraints, modeling each UAV as an electric eel with random initial positions and velocities. This algorithm simulates the interaction, resting, hunting, and migrating behaviors of electric eels during their foraging process. During the interaction phase, UAVs engage in global exploration through communication and environmental sensing. The resting phase allows UAVs to temporarily hold their positions, preventing premature convergence to local optima. In the hunting phase, the swarm identifies and pursues optimal paths, while in the migration phase the UAVs transition to target areas, avoiding threats and obstacles while seeking safer routes. The algorithm enhances overall optimization capabilities by sharing information among surrounding individuals and promoting group cooperation, effectively planning flight paths and avoiding obstacles for precise strikes. The MATLAB(R2024b) simulation platform is used to compare the performance of five optimization algorithms—SO, SCA, WOA, MFO, and HHO—against the proposed Electric Eel Foraging Optimization (EEFO) algorithm for UAV swarm target strike missions. The experimental results demonstrate that in a sparse undefended environment, EEFO outperforms the other algorithms in terms of trajectory planning efficiency, stability, and minimal trajectory costs while also exhibiting faster convergence rates. In densely defended environments, EEFO not only achieves the optimal target strike trajectory but also shows superior performance in terms of convergence trends and trajectory cost reduction, along with the highest mission completion rate. These results highlight the effectiveness of EEFO in both sparse and complex defended scenarios, making it a promising approach for UAV swarm operations in dynamic urban environments.

Due to the complex electrochemical reactions within lithium-ion batteries and the uncertainties with respect to external environmental factors, accurately assessing their State of Health (SOH) remains a significant challenge. To improve the precision of SOH estimation, we propose an intelligent estimation approach that integrates data visualization and advanced machine learning techniques. Initially, the battery data are visualized using matplotlib to extract key features such as temperature difference, voltage difference, and average voltage. Subsequently, an XGBoost-based model is constructed to perform the initial SOH estimation. To further enhance the estimation accuracy, we introduce the Autoregressive Integrated Moving Average Model (ARIMA) model for post-estimation correction, effectively refining the preliminary results. Experimental results demonstrate that the proposed XGBoost–ARIMA model outperforms traditional algorithms, including Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), not only in estimation accuracy but also in generalization capability, showing significant improvements over five other regression models.

With the rapid advancement of unmanned aerial vehicle (UAV) technology, its high mobility and ease of deployment have demonstrated tremendous potential in integrated sensing and communication (ISAC) systems. However, as user demands diversify, network architectures become increasingly distributed, and as the number of UAVs grows rapidly, ISAC systems face significant challenges in optimizing communication and sensing resources. In particular, UAV communication in collaborative UAV missions is highly susceptible to hostile signal disruptions, leading to degraded communication quality, weakened sensing performance, resource wastage, and increased energy consumption. To address these challenges, this paper proposes a game-theoretic optimization method for multi-UAV ISAC networks. First, a multi-UAV communication-sensing network model is constructed to characterize the impact of interference sources on communication and sensing performance. Based on this model, under energy constraints, a joint optimization of transmission power and UAV trajectory is performed. The problem is formulated as a utility maximization framework for the ISAC network and modeled as a game-theoretic approach. The proposed model is rigorously proven to be an exact potential game, ensuring the existence of at least one pure-strategy Nash equilibrium. To solve for the equilibrium, a distributed optimization algorithm—the Electric Eel Foraging Optimization (EEFO) algorithm is developed. Simulation results validate the effectiveness of the proposed method, showing that it significantly enhances the communication and sensing performance of multi-UAV networks while effectively reducing energy consumption. This work provides a novel solution to the resource optimization challenges in multi-UAV ISAC networks, offering both theoretical and practical contributions to advance ISAC technology.

With the rapid popularization of Internet of Things (IoT) devices, the freshness of data has become a key factor affecting decision quality and system efficiency. The application of unmanned aerial vehicle (UAV) technology provides a new solution for IoT data collection. This article mainly studies how multiple UAVs can improve the freshness of IoT data collection through joint optimization of trajectory planning and channel selection in a three-dimensional (3D) interference environment. We conducted markov decision process (MDP) modeling on the combinatorial optimization problem of the model and proposed an intelligent joint trajectory planning and channel selection for data collection (ITPCS-DC) algorithm based on multi-agent deep reinforcement learning (MADRL). This algorithm can not only avoid the agent falling into local optimum caused by 3D interference, but also effectively reduce the age of information (AoI) of IoT data collection. Simulation results show that the proposed ITPCS-DC algorithm can achieve higher rewards, lower average AoI, reduced channel switching costs, and shorter trajectory lengths compared to other benchmark algorithms. Moreover, it has better adaptability to more complex collaborative environments.