Search Results (772)

This paper presents a hybrid algorithmic framework for nonparametric multisite daily streamflow simulation, evaluated on 52 years of observed data from three hydrological stations. The method generates streamflow data of 1000 synthetic years while preserving marginal distributions, daily rank structures, inter-station consistency, annual-sum behavior, and dependence across consecutive years. The workflow integrates Monte Carlo sampling, Schaake-shuffle reordering, block-mosaic reconstruction with partial freezing, Hungarian assignment optimization, annual-sum matching, and an adaptive permutation genetic algorithm for year-order optimization. The results show that the proposed algorithm improves aggregate hydrological diagnostics, particularly annual-sum autocorrelation, hydrological indices, persistence, seasonality, and timing of extremes, while reducing runtime in the final optimization phase by 45.2% compared to the benchmark algorithm. The study therefore formulates daily streamflow simulation as a constrained time-series reconstruction and permutation-optimization problem, making the method suitable for further algorithmic development and other multisite environmental time-series applications. Full article

Earth observation satellite constellations must ensure rapid and reliable coverage of critical areas under propulsion constraints and environmental variability. Existing studies typically treat constellation geometry, maneuver optimization, and operational assessment as separate problems, limiting understanding of the structural origin of performance constraints. This study proposes a three-stage hierarchical optimization framework that separates intrinsic geometric potential, reactive multi-objective maneuver adaptability, and stochastic operational realizability. In Stage 1, a genetic algorithm optimizes constellation geometry with respect to global success rate across multiple areas of interest. In Stage 2, NSGA-II-based maneuver optimization evaluates the trade-off between success rate, time to first access, and total $\mathsf{\Delta }V$ expenditure. In Stage 3, operational performance is assessed using the Operational Imaging Response Time (OIRT) metric under probabilistic environmental filtering. Simulation results show that constellation geometry is the primary structural determinant of performance within the analyzed configuration range, while maneuver optimization provides moderate but measurable response-time improvements. Environmental filtering reduces achievable coverage relative to purely geometric predictions. The proposed framework establishes a consistent methodology linking constellation synthesis, maneuver planning, and operational response evaluation for time-critical Earth observation missions. Full article

This paper investigates the coordinated deployment and routing of communication equipment by a Sub-mother UAV swarm in power-grid emergency communication scenarios. Considering mission timeliness and payload constraints, a heterogeneous MUAV–SUAV coordinated deployment-and-routing model is established to minimize the total system cost, including platform flight cost, SUAV activation cost, and penalty cost caused by delayed deployment. To solve this problem, a two-stage optimization framework is proposed. In the first stage, an improved K-means clustering algorithm with neighborhood search (K-means-NS) is developed to divide deployment points into feasible sub-regions while satisfying SUAV endurance constraints and maintaining the deployment–retrieval payload balance required by the MUAV. In the second stage, the MUAV inter-region visiting sequence is treated as a routing subproblem, and an improved adaptive genetic algorithm (IAGA) is designed to optimize the coordinated routes of the MUAV and SUAVs within each sub-region. The IAGA adopts hybrid encoding, feasible-solution adjustment, elitist selection, and adaptive crossover–mutation operations to improve search efficiency under complex constraints. Numerical experiments on small-, medium-, and large-scale scenarios show that the proposed method can generate feasible sub-region divisions and coordinated routing schemes. Compared with GA and G-PSHA, IAGA reduces the total flight cost by approximately 21.2%, 10.5%, and 23.2% relative to GA and by approximately 0.2%, 2.5%, and 8.1% relative to G-PSHA in the three scenarios, respectively. Sensitivity analysis further indicates that stricter mission-timeliness requirements increase penalty costs, highlighting the importance of timely communication-device deployment in emergency restoration. Full article

To address the limited assistive performance and insufficient individual adaptability of hip exoskeletons, a multi-objective optimization-based assistive strategy is proposed. A parameterized assistive torque model is constructed based on human gait characteristics, with the objectives of reducing joint load and improving human–robot interaction coordination. The NSGA-II (Non-dominated Sorting Genetic Algorithm II) is employed to optimize the assistive parameters, and the optimized results are implemented in a gait phase-based control method to achieve synchronized torque output over the gait cycle. Experimental validation is conducted on a hip exoskeleton platform using motion capture and electromyography measurements. The results demonstrate that the proposed method effectively reduces hip joint torque, decreases muscle activation levels, and enhances human–robot interaction performance. Full article

The rapid growth of sophisticated malware, including polymorphic, metamorphic, and zero-day threats, has made traditional signature-based and heuristic detection methods increasingly insufficient in modern desktop computing environments. As cyber threats continue to evolve in both complexity and scale, the demand for intelligent and adaptive malware detection mechanisms capable of identifying previously unseen attacks has become more critical than ever. In this study, we propose a novel deep learning framework for three-dimensional malware image classification that utilizes visual representation learning to improve malware detection performance. The proposed framework converts raw malware binaries into three-dimensional grayscale and RGB image representations, allowing hidden structural and spatial patterns within malware samples to be analyzed more effectively. By transforming malware data into multi-dimensional visual forms, the proposed system facilitates the process of automatically learning hierarchical features by CNN through multi-dimensional visualization of malware binary codes. In addition, an optimization technique using Genetic Algorithms is implemented within this architecture to improve classification performance and stability. The proposed evolutionary algorithm performs an effective search process within the large parameter space of 3D-CNN, leading to the identification of models that facilitate learning. It is shown that multi-dimensional visualization of malware achieves improved classification performance. It can be concluded that the combination of three-dimensional malware visualization, deep learning, and genetic optimization is promising for the development of future intelligent malware detection tools. Full article

Namibia’s rural communities continue to experience limited and unreliable electricity access despite the potential of the country’s exceptional solar, wind, and biomass renewable energy resources. Conventional grid extension remains financially and technically impractical for dispersed off-grid settlements, underscoring the need for cost-effective, renewable-based alternatives. This paper presents a resource-driven design and multi-objective optimization framework for Hybrid Renewable Energy Systems (HRESs) tailored to Namibia’s off-grid communities. The proposed model integrates solar PV, wind turbines, biomass generators, and hydrogen-based fuel cells with a hybridized energy storage consisting of batteries, supercapacitors, and hydrogen tanks. Using the Non-dominated sorting Genetic Algorithm-II (NSGA-II), the system simultaneously minimizes Total Life Cycle Cost (TLCC), Levelized Cost of Electricity (LCOE), Loss of Power Supply Probability (LPSP), carbon dioxide (CO₂) emissions, and Wasted Renewable Energy (WRE). The framework is applied to three rural villages, Oluundje, Ombudiya, and Onguati, using high-resolution, site-specific renewable resource datasets and community-level load forecasts. The results demonstrate that resource-aligned configurations substantially improve system reliability (up to 99.28%), reduce LCOE (0.0023–0.0811 USD/kWh), and optimize dispatch behaviour across seasonal variations. Storage hybridization further enhances stability by balancing transient and long-duration deficits. Compared to existing diesel mini-grids, the optimized HRESs achieve markedly superior techno-economic and environmental performance. The proposed framework offers a scalable, adaptable, and policy-ready tool for accelerating sustainable rural electrification in Namibia. Full article

With the rapid growth of data center (DC) energy consumption and the large-scale integration of renewable energy, DCs increasingly face time-varying power upper-bound constraints jointly shaped by grid power supply capability, renewable energy fluctuations, and demand response mechanisms. Meanwhile, DC power consumption exhibits a typical information-load-driven characteristic. The computing tasks hosted by virtual machines affect server-side IT power consumption through resource utilization states such as CPU, memory, disk I/O, and network I/O, and are further coupled with non-IT auxiliary power consumption from cooling, power distribution, and networking equipment. In such cyber–physical operation scenarios, physical-layer sensing data and hypervisor-level virtualization monitoring data jointly provide the state basis for power estimation, power warning, and migration decisions. To address the mismatch between dynamic power upper bounds and time-varying information loads, this paper investigates the information load scheduling problem under constrained power loads and proposes a two-stage virtual machine (VM) migration optimization framework. In the VM selection stage, a Multi-Factor Balanced (MFB) algorithm is designed. By introducing a warning-line trend model based on the arctangent function, MFB comprehensively considers resource utilization, power load variation trends, and service level agreement (SLA) violation levels to dynamically identify candidate VMs for migration. In the VM placement stage, a Multi-Factor Equilibrium Ant Colony Optimization (MFEACO) algorithm incorporating a Random Roulette Wheel (RRW) selection mechanism is proposed. By constructing normalized multi-dimensional equilibrium factors, MFEACO coordinates the trade-off among energy consumption, load balancing, and SLA violations. Simulation experiments are conducted on an improved CloudSim platform using real-world cluster trace data from Google and Alibaba. The results show that, while satisfying dynamic power constraints, the proposed MFB–MFEACO framework achieves a favorable comprehensive trade-off among energy consumption control, SLA violation suppression, and migration reduction. Compared with traditional heuristic methods and a power-constrained genetic algorithm baseline, the proposed framework demonstrates better dynamic adaptability and scheduling stability. Full article

This paper addresses the bidirectional multi-chamber lock scheduling problem by formulating a multi-objective mixed-integer linear programming (MILP) model that simultaneously minimizes average ship waiting time and maximizes chamber utilization. A tailored adaptive large neighborhood search (ALNS) algorithm is developed specifically based on the principle of the destruction and reconstruction of solutions. The algorithm efficacy is validated using the real-word data from Huai’an Lock of the Subei canal. The scheduling rules and parameters are defined from practical operation records. Simulation results demonstrate that the ALNS-based optimization significantly improves lock performance with average chamber utilization increasing by 12.98% and waiting time decreasing by 44.40%. Sensitivity analyses on objective weights further confirm the robustness of the proposed method. Benchmark comparisons with a greedy heuristic, genetic algorithm (GA), and particle swarm optimization (PSO) highlight the effectiveness and computational efficiency of ALNS. This study further explores a threshold-based directional control strategy, showing that relaxing strict alternating-direction rules under asymmetric traffic demand can improve efficiency. The findings provide practical insights for lock scheduling, offering decision support for lock authorities in designing adaptive scheduling and directional control policies. Full article

Considering that conventional heavy-duty mining trucks equipped with centralized drive systems suffer from low transmission efficiency and limited flexibility in power distribution, this study focuses on distributed independent-drive heavy-duty mining trucks and develops energy-saving control strategies from two perspectives: drive torque control and regenerative braking. For the drive torque control, based on the principle of optimal driving efficiency, the overall efficiency of the drive motors is selected as the objective function, and an adaptive genetic algorithm (AGA) is employed to optimize the torque distribution coefficients among the axles offline. For regenerative braking, a fuzzy-control-based electromechanical braking distribution strategy and a dynamic-load-based inter-axle braking force allocation strategy are proposed. Finally, a co-simulation was conducted using MATLAB/Simulink and TruckSim based on specific open-pit mining conditions. Compared with the conventional baseline without energy-saving control, the simulation results demonstrate that under the single-cycle operation, the proposed strategy increases the driving energy utilization rate by 5.69% and achieves a braking energy recovery rate of 39.41%. Furthermore, under the full-mine cyclic operation, the proposed strategy extends the vehicle’s operational duration on a single charge by 200%. These findings demonstrate the strong potential of the proposed strategy to improve overall driving efficiency and fully exploit the regenerative braking capabilities of heavy-duty mining trucks, thereby providing theoretical support for enhancing their economic efficiency and driving range. Full article

Quantum convolutional neural networks (QCNNs) are emerging as promising models in quantum machine learning, particularly for image classification and computer vision tasks. Recent developments include hybrid classical–quantum architectures, advanced quantum encoding methods, and novel circuit designs that improve data processing on Noisy Intermediate-Scale Quantum (NISQ) devices. However, practical implementation remains challenging due to circuit complexity, gate count, qubit connectivity, and hardware noise, which limit scalability and performance. Consequently, quantum circuit optimization has become essential for reducing resource requirements and improving classification accuracy. This study presents a systematic literature review of 40 research papers published between 2014 and 2025. The review covers QCNNs together with closely related quantum neural network (QNN) models and quantum circuit optimization studies, since circuit-optimization techniques are frequently developed for QNNs more broadly rather than for QCNN architectures in isolation. Within this scope, it examines network architectures, encoding strategies, application domains, and optimization techniques, with particular attention to heuristic and metaheuristic approaches such as genetic algorithms and evolutionary strategies. The findings highlight growing trends in hybrid quantum–classical integration, the widespread adoption of metaheuristic optimization, and the importance of multi-objective frameworks adapted to quantum hardware constraints. Finally, the review identifies key research gaps and future directions for practical QCNN deployment on near-term quantum devices. Full article

The vertical landing of quadrotor unmanned aerial vehicles (UAVs) involves highly transient impact dynamics that generate significant vibrations on the UAV body, particularly under uncertain touchdown conditions such as uneven terrain, asymmetric ground contact, and high-impact landing. In this study, a robust semi-active vibration control framework is proposed for a quadrotor UAV equipped with a four-point soft landing gear system. The UAV is modeled as a three-degree-of-freedom rigid body including heave, pitch, and roll motions, while each landing gear leg is represented by an equivalent spring-damper mechanism with adaptively controllable damping characteristics. To evaluate the effectiveness of the proposed framework, PID (Proportional–Integral–Derivative), GA-PID (Genetic Algorithm-Based Proportional–Integral–Derivative), Fuzzy–PID (Fuzzy Logic-Based Proportional–Integral–Derivative), and ANFIS-PID (Adaptive Neuro-Fuzzy Inference System-Based Proportional–Integral–Derivative) controllers are comparatively investigated under five different landing scenarios. The nonlinear touchdown dynamics are implemented in the MATLAB/Simulink environment using a state-space-based simulation model. The results demonstrate that intelligent adaptive control methods significantly improve landing stability and vibration attenuation compared to the conventional PID controller. Among all methods, the ANFIS-PID controller achieved the best overall performance. Under the most severe landing condition, the peak vertical displacement was reduced from 0.114 m to 0.025 m, while the maximum pitch and roll angles decreased from approximately 11° to nearly 2°. Additionally, the settling time was reduced from nearly 10 s to below 3 s. Full article

Passive reception of spaceborne synthetic aperture radar (SAR) signals is of great significance for acquiring target characteristics and identifying SAR operating states. With the rapidly growing demand for high-quality SAR signal reception, signal-receiving arrays are prone to beam performance deterioration and difficulty in beamforming under wide-angle scanning conditions. Traditional uniform arrays fail to meet practical engineering requirements and cannot balance multiple conflicting performance indicators. To address the above technical bottlenecks, this paper proposes a design method of a non-uniform planar receiving array based on the MOEA/D-HPSO algorithm. Taking maximum sidelobe level (MSL), array gain (G), and beamwidth (BW) as core performance indicators, a multi-objective optimization model of SAR signal-receiving array for wide-angle scanning is established. This method integrates the multi-objective decomposition strategy and hybrid genetic particle swarm optimization mechanism, decomposes complex multi-objective problems into several scalar subproblems, obtains uniformly distributed Pareto fronts, and effectively improves the diversity of solution sets. Simulation experimental results show that the proposed algorithm is superior to traditional mainstream algorithms such as NSGA-II and MOEA/D-DE in terms of convergence accuracy, solution set distribution, and various performance indicators. Typical array design examples verify that the proposed method can adapt to various engineering application scenarios and provide technical support for spaceborne SAR signal reception and spectrum management. Full article

Rapid and non-destructive estimation of maize (Zea mays L.) leaf flavonoid (Flav) content is important for crop stress monitoring and precision agriculture. This study aimed to improve Flav estimation by integrating unmanned aerial vehicle (UAV)-based multispectral data, texture features, and phenological parameters across six key growth stages in the Guanzhong Plain, China. Maize Flav content was measured in situ using a Dualex Scientific+ meter, while canopy reflectance was acquired with a DJI M300 RTK UAV equipped with an MS600 Pro multispectral camera. A comprehensive feature set, including spectral bands, vegetation indices, texture features, texture indices, and logistic curve-derived phenological parameters, was constructed. Three feature selection methods, competitive adaptive reweighted sampling (CARS), the genetic algorithm (GA), and the successive projections algorithm (SPA), together with three regression models, partial least squares regression (PLSR), extreme gradient boosting (XGBoost), and convolutional neural network (CNN), were evaluated for Flav estimation. The results showed that integrating spectral, texture, and phenological information significantly improved model performance compared with spectral variables alone. CNN and XGBoost generally outperformed PLSR. Across the six growth stages, the stage-specific optimal models achieved coefficient of determination (R²) values ranging from 0.7749 to 0.8686 and residual prediction deviation (RPD) values ranging from 2.0046 to 2.6019, indicating high to outstanding predictive ability. The highest accuracy was obtained at R3 using the CARS-X_II-CNN model, with R² = 0.8686, root mean square error of validation (RMSEV) = 0.0382, and RPD = 2.6019. Texture features and phenological metrics, especially the start of season derived from the normalized difference vegetation index (NDVI_SOS) and the rate of senescence derived from the enhanced vegetation index (EVI_ROS), contributed substantially to model accuracy. In addition, maize Flav showed a unimodal response to nitrogen supply, with moderate nitrogen levels associated with higher Flav content. This study demonstrates the potential of UAV-based multisource feature integration and machine learning for accurate maize Flav estimation, and provides a useful framework for digital crop phenotyping and stress diagnosis. Full article

Metaheuristic algorithms are widely used to find optimal or near-optimal solutions for complex problems by taking inspiration from natural behaviors and processes. Although many different methods have been developed, a common problem in many of them is maintaining a good balance between exploration and exploitation and avoiding local optima. To deal with this issue, this paper proposes a new method called the Blackcap Optimization Algorithm (BCOA), which is inspired by the navigation and migration behavior of Blackcap birds. Instead of using complicated distance calculations, the proposed method is based on angular movement vectors. The movement of each search agent is controlled by an angle-based mathematical model that combines the global best angle, a successful neighboring angle, and an adaptive exponential disturbance factor. In addition, the algorithm uses a quasi-genetic path transition mechanism to combine successful parent paths together, along with a territorial competition stage. This structure helps reduce computational cost and improves the balance between exploration and exploitation. The performance of the proposed algorithm is tested on 32 benchmark functions and seven engineering and network optimization problems. The simulation results show that BCOA has a good ability to avoid local optima and can achieve acceptable convergence speed and cost reduction compared to several existing methods. Full article

Adverse weather conditions, particularly thunderstorms, are the primary cause of flight delays and safety threats, accounting for approximately 58.7% of irregular flights in 2025. Traditional static rerouting methods often fail to adapt to the non-linear evolution of convective weather. This paper proposes a high-fidelity dynamic rerouting framework to enhance flight safety and efficiency. In the perception layer, a RainNet deep learning model is employed for short-term recursive nowcasting of radar reflectivity, which is subsequently transformed into Dynamic Avoidance Zones (DAZ) via clustering and convex hull algorithms. In the decision layer, a two-stage improved Genetic Algorithm (GA) is developed to solve the rerouting path. The first stage generates initial collaborative solutions under a receding-horizon framework, while the second stage applies a “path-straightening” module to reduce cumulative turning angles and curvature fluctuations. The comparative results in actual scenarios demonstrate a distinct dual-advantage over baseline methodologies. Compared to sampling-based strategies, the proposed model reduces the path length by 14.79%. Furthermore, when compared to heuristic algorithms, it actively trades a negligible 1% distance margin to achieve a massive 92.7% reduction in the cumulative turning angle. With a maximum single turn of only 32.51°, the trajectory completely eliminates sawtooth jitter and redundant detours. Ultimately, this research provides essential technical support for improving air traffic management efficiency and reducing controller workload during severe weather events. Full article