Search Results (510)

The advancement of precision agriculture and smart tillage relies on high-precision, real-time perception of unmanned ground vehicle (UGV) positions. In large-scale farmland operations, conventional Global Navigation Satellite System (GNSS)-based positioning may suffer from short-term signal loss, degrading accuracy. Multi-unmanned aerial vehicle (UAV)-assisted vehicle localization based on direction-of-arrival (DOA) estimation can provide critical positioning compensation for UGV, where compressed sensing (CS)-based DOA-assisted localization algorithms are commonly employed. However, existing schemes neither account for the bias induced by the local positional oscillation of UAVs, nor address the limited accuracy and real-time performance of CS-based DOA estimation, restricting their agricultural deployment. To this end, this paper first develops an assisted-localization architecture that explicitly incorporates the local positional offsets of multiple UAVs, together with a corresponding array signal reception model. To overcome the accuracy–efficiency trade-off of conventional CS-DOA methods, an adaptive local overcomplete dictionary (LOD) is then constructed to robustly refine the angular resolution around the region of interest. With the number of sources K assumed to be known and fixed, a particle swarm optimization (PSO)-based local refinement algorithm is further introduced to adaptively optimize the DOA estimates within the constructed local dictionary, thereby improving estimation robustness under low-SNR and coherent-source conditions. Consequently, the proposed method improves robustness while maintaining favorable localization accuracy and computational efficiency in the simulated scenarios. Simulation results show that it substantially reduces localization error compared with state-of-the-art algorithms, suggesting its potential as a localization-assistance approach for UGV navigation in sustainable tillage. Full article

Desert highways have long been subjected to aeolian sand hazards, and sand accumulation on the pavement significantly weakens the surface texture and deteriorates skid resistance, which has become one of the core contributing factors to traffic accidents on desert road sections. Current research predominantly focuses on the attenuation law of the macroscopic friction coefficient of sand-covered pavements; however, the quantitative correlation mechanism between three-dimensional micro-texture characteristics and skid resistance has not been sufficiently revealed, and there is a lack of high-precision skid resistance prediction methods under multi-condition coupling scenarios. To address the above research deficiencies, this paper takes the asphalt pavement in the Tengger Desert region as the research object. A handheld three-dimensional texture scanning system was employed to acquire the three-dimensional pavement morphology parameters under different sand coverages, and the sideway force coefficient (SFC) was synchronously measured under the corresponding conditions. Through Pearson correlation analysis and dual multiple comparison correction using the FDR-BH and Bonferroni methods, the core influencing indicators were identified. Subsequently, a skid resistance prediction model based on a BP neural network optimized by the particle swarm optimization (PSO) algorithm was constructed and horizontally compared and validated with LSTM and PSO-SVM models. The research results show the following: ① under dry conditions, the root mean square height ($S_q$), peak density ($S_{pd}$), arithmetic mean peak curvature ($S_{pc}$), valley void volume ($V_{vv}$), root mean square slope ($S_{dq}$), and developed interfacial area ratio ($S_{dr}$) are significantly linearly correlated with the SFC, among which $S_q$, $S_{pd}$, $S_{pc}$, and $V_{vv}$ are the core controlling indicators, with the absolute values of their correlation coefficients all exceeding 0.73, and ② the constructed PSO-BP prediction model achieved a coefficient of determination $R^2$ of 0.86093 on the test set, and its prediction accuracy and generalization ability are both superior to those of the LSTM and PSO-SVM models, enabling it to effectively characterize the nonlinear mapping relationship between multiple texture parameters and skid resistance. This study can provide theoretical support and a technical basis for skid resistance evaluation, sand accumulation disaster warning, and scientific maintenance decision-making for desert highways. Full article

Energy system optimization seeks to utilize multiple energy sources efficiently under technical, economic, and environmental constraints. The increasing integration of renewable energy and the need for sustainable operation have made the optimal planning and management of hybrid energy systems crucial. Classical optimization methods, including Linear Programming, Nonlinear Programming, and simulation-based models, often face limitations when addressing high-dimensional and nonlinear problems. This study introduces a deep learning–based surrogate modeling framework for sizing the components of hybrid renewable energy systems. Initially, Particle Swarm Optimization (PSO) is employed to determine the optimal component sizes for a large number of synthetically generated hourly solar irradiance and load profiles. These optimal solutions are then used as target labels. The associated annual time-series data are transformed into multi-channel Data Map (DMAP) images, which serve as inputs for convolutional neural networks (CNNs). After training, the CNN models are capable of directly estimating the required number of photovoltaic (PV) panels, inverter capacity, and battery units from the DMAP images, eliminating the need to perform the iterative PSO optimization during the prediction stage. Various convolutional neural network architectures, including ResNet, DenseNet121, RegNet, ConvNeXt, EfficientNet, SqueezeNet, MobileNet, and InceptionV3, were evaluated for this multi-output regression task. The results indicate that ResNet and DenseNet121 achieve the best performance, while ConvNeXt provides strong results with a modern architectural design. Among the evaluated models, DenseNet121 achieved coefficients of determination (R²) of 0.934, 0.988, and 0.947 for predicting the sizes of the PV array, inverter, and battery bank, respectively. These results correspond to an average prediction accuracy of approximately 90.6%. ResNet produced similar performance, with its highest R² value reaching 0.983 for inverter sizing. Lightweight networks such as SqueezeNet and MobileNet demonstrate notable effectiveness for resource-constrained systems, whereas InceptionV3 underperforms in leveraging its multi-scale architecture. These results demonstrate that, once the models have been trained, deep learning–based surrogate models can generate sizing decisions comparable to those obtained using PSO with only a fraction of the computational effort. As a result, they provide a fast and practical alternative to conventional iterative optimization methods for component sizing in smart grid and sustainable energy planning applications. Full article

This paper proposes a multi-strategy-enhanced Octopus Optimization Algorithm (OOA) for hyperparameter optimization in hyperspectral image classification. Hyperspectral images pose significant challenges due to their numerous spectral bands, high dimensionality, and complex spectral differences between classes, which complicate classification modeling. The classification performance of support vector machine (SVM) classifiers is also highly dependent on parameter settings. The original OOA is extended by incorporating an initialization strategy based on elite backpropagation, a multi-stage nonlinear adaptive parameter control mechanism, an elite-guided differential mutation strategy, a Lévy flight restart mechanism with stagnation monitoring, and a stable boundary handling strategy. These enhancements constitute the IOOA-SVM parameter optimization framework. The proposed method is evaluated against OOA, Particle Swarm Optimization (PSO), Sand Cat Swarm Optimization (SCSO), Salp Swarm Algorithm (SSA), Grey Wolf Optimizer (GWO), Arithmetic Optimization Algorithm (AOA), Differential Evolution (DE) and Linear Population Size Reduction Success-History Based Adaptive Differential Evolution (L-SHADE) on the CEC2017 test set, achieving superior results on most of the 29 test functions, IOOA achieved the best results on average for 27 of the 29 test functions, outperforming the original OOA on all 29 test functions and demonstrating superior performance on most stability metrics. Different improvement strategies yield varying degrees of performance gains for the algorithm; among them, the elite-guided differential mutation strategy produces the most significant performance improvement. The synergy and complementarity among multiple strategies play a major role in enhancing the performance of the Improved Octopus Optimization Algorithm. Experimental results show that the SVM classifier optimized using the improved OOA achieves a classification accuracy of 97.3731%, representing a 0.2278 percentage point improvement over the original algorithm and demonstrating strong overall optimization performance. Full article

In complex nonlinear steel frames, the limit-state function is typically nonlinear and implicit, which hinders the direct application of traditional first- and second-order reliability methods (FORM/SORM). A reliability analysis approach for steel frames was developed by reconstructing the limit state function using a support vector machine (SVM). Finite-element response samples were employed to construct an explicit SVM surrogate model of the limit state function. The particle swarm optimization (PSO) is adopted to globally optimize the model hyperparameters, thereby enhancing the predictive accuracy and stability of the reconstructed limit-state function. The reconstructed limit-state function was subsequently incorporated into the FORM and SORM frameworks to enable the efficient evaluation of failure probabilities and reliability indices for steel frames. The proposed approach is validated through multiple benchmark examples, including explicit nonlinear limit-state functions and multistory multispan rigid frame structures. The results indicate that the SVM-reconstructed limit-state function accurately captures the non-linear characteristics of the structural responses. The corresponding reliability estimates were in good agreement with the Monte Carlo simulation (MCS) results and previously reported findings. Compared with FORM, SORM significantly improves the accuracy of reliability and failure probability estimation, with the Tvedt formulation demonstrating the highest level of consistency. Full article

UAV path planning in complex urban environments faces significant challenges due to dense obstacles, narrow corridors, and stringent safety requirements. To address these issues, this paper proposes LLSOA, a modified Seagull Optimization Algorithm that integrates Latin Hypercube Sampling (LHS) for population initialization and Lévy Flight for global search. The key innovation lies in the problem-driven design: LHS ensures uniform coverage in dense urban maps, while Lévy Flight helps escape local optima. Compared with four state-of-the-art swarm intelligence algorithms (DBO, GWO, PIO, and PSO) across four urban scenarios, LLSOA achieves the best comprehensive fitness. Considering multiple constraints including path length, curvature, collision avoidance, and obstacle-avoidance logic, the trajectories generated by LLSOA show competitive overall performance, with no unsafe points recorded in the test scenarios and the best fitness values among the compared algorithms, albeit with a slight trade-off in path length. High-fidelity AirSim simulations with GPS/IMU noise further demonstrate that the planned trajectories remain within engineering acceptable limits. Compared with the noise-free baseline, the maximum trajectory deviation increases by 2.3% and the average deviation increases by 2.8% under high GPS/IMU noise. The main contributions are: (1) a problem-driven LLSOA that combines LHS and Lévy Flight, specifically tailored to dense urban environments; (2) theoretical analysis and simulation verification demonstrating its feasibility for multi-constraint path planning under the tested conditions; (3) high-fidelity (UE+AirSim) validation showing that the generated trajectories retain stability even under realistic sensor noise. Full article

Leaf nitrogen content (LNC) is an important physiological indicator for evaluating citrus nutritional status, photosynthetic capacity, and fertilization demand. However, conventional LNC determination mainly relies on field sampling and laboratory chemical analysis, which are destructive, time-consuming, labor-intensive, and limited in spatial continuity, making them unsuitable for large-scale real-time nitrogen monitoring in complex orchard environments. To achieve rapid and non-destructive estimation of citrus LNC, this study developed a UAV multispectral inversion framework integrating object-based canopy extraction and machine learning models. Field experiments were conducted in a citrus orchard in western Hubei Province, China. Multi-temporal UAV multispectral images were collected from April to October 2025, and ground measurements of citrus LNC were collected simultaneously. First, minimum distance classification (MDC), maximum likelihood classification (MLC), and object-based image analysis (OBIA) were used for land-cover classification of citrus orchard images, and their canopy extraction performance under complex orchard backgrounds was compared. Subsequently, multiple vegetation indices were calculated from the extracted citrus canopy spectra, and sensitive spectral features were selected through correlation analysis. Finally, seven models, including simple linear regression, quadratic regression, partial least squares regression (PLS), back propagation neural network (BP), extreme learning machine (ELM), particle swarm optimization-extreme learning machine (PSO-ELM), and particle swarm optimization-back propagation neural network (PSO-BP), were constructed to systematically evaluate the inversion performance of citrus LNC across the entire growth period. The results showed that: (1) OBIA achieved higher classification accuracy and temporal stability in citrus orchard land-cover classification, with overall accuracy ranging from 68.86% to 85.65% and Kappa coefficients ranging from 0.56 to 0.72, outperforming MDC and MLC. This indicates that OBIA can effectively reduce the interference of bare soil, grass, shadows, and other non-target objects on canopy spectral extraction. (2) The correlations between vegetation indices and LNC varied markedly among different growth stages, suggesting that the spectral response of citrus LNC has strong phenological dependence and that a single vegetation index is insufficient to stably characterize LNC variation across the whole growth period. (3) At the whole-growth-period scale, multi-index fusion models outperformed single-index models, among which EVI, TVI, and MTVI showed relatively strong cross-stage sensitivity. (4) Optimized machine learning models generally outperformed traditional regression models and unoptimized machine learning models. Among them, PSO-BP achieved the best performance, with a validation R² of 0.68 and an RMSE of 1.54 g kg⁻¹, representing an increase in R² of 23.64% compared with the PLS model and 25.93% over the baseline BP model in terms of R². Overall, this study demonstrates that OBIA-based canopy spectral quality improvement combined with PSO-optimized machine learning can effectively improve the stability and reliability of UAV multispectral estimation of citrus LNC under complex orchard backgrounds. The proposed framework provides technical support for citrus nitrogen diagnosis, precision fertilization, and intelligent orchard management. Full article

Mobile edge computing (MEC) enables resource-constrained user devices (UDs) to obtain low-latency computing services by offloading computational tasks to the network edge. Non-orthogonal multiple access-enabled mobile edge computing (NOMA-MEC) systems feature asymmetric states across UDs, dynamic task arrivals, and competition for wireless and edge computing resources. Under these conditions, offloading decisions affect device energy consumption, task delay, and edge computing resource allocation, making long-term system optimization difficult. This study jointly optimizes task offloading and system resource scheduling to minimize the long-term delay–energy cost. The problem is formulated as a partially observable Markov decision process (POMDP) and addressed using a master-refined multi-agent proximal policy optimization (MR-MAPPO) algorithm. MR-MAPPO combines continuous action relaxation, master action refinement, and a behavior cloning auxiliary term to learn policies in a hybrid discrete–continuous action space. A marginal congestion delay term is also introduced to capture the impact of newly admitted tasks on existing edge workloads. Simulation results show that MR-MAPPO outperforms the considered baselines, while ablation studies verify the effects of its key components. Under the main experimental setting, MR-MAPPO reduces the system cost by 17.9% and 22.9% relative to standard MAPPO and particle swarm optimization (PSO), respectively. Full article

With the rapid growth of electric vehicles (EVs), high charging demand has increased uneven station utilization and pressure on distribution networks. Therefore, this paper proposes a collaborative method for capacity planning and charging scheduling of multiple charging stations (CSs) considering time-of-use (TOU) pricing and energy storage systems (ESSs). A total system cost model is established, including transformer and ESS costs. Secondly, a deep neural network-guided improved sparrow search algorithm (DNN-ISSA) is proposed to optimize the number of chargers and parking spaces by predicting the initial capacity center. Furthermore, a charging scheduling algorithm is proposed to optimize user charging time by introducing a TOU price response function to modify charging probabilities. A case study of 36 CSs in Jinan shows that the proposed method reduces average charging time by 15.7, 15.4, and 15.2 min for 1000, 5000, and 10,000 demand points, while lowering the total system cost from 73.92 to 70.36 million yuan. The convergence value of DNN-ISSA reduces by 15.05%, 21.67%, and 11.61% compared with the improved sparrow search algorithm (ISSA), particle swarm optimization algorithm (PSO), and sparrow-particle swarm optimization algorithm (SSA-PSO), respectively. The proposed method enhances energy utilization, mitigates peak loads, and supports low-carbon EV charging operation. Full article

High photovoltaic (PV) penetration introduces rapid variability, voltage deviations, and increased real-power losses in distribution networks, necessitating control strategies that remain effective under forecast uncertainty. This paper presents a unified Particle Swarm Optimization-based Receding-Horizon Control (PSO-RHC) framework for optimal coordination of multiple Battery Energy Storage Systems (BESSs) in a PV-rich distribution feeder. The controller employs a receding-horizon structure—using horizon-based forecasts, constraint enforcement, and stepwise decision updates—while PSO serves as the optimization engine that computes BESS power setpoints at each prediction step. Deterministic PV and load forecasts are perturbed with stochastic noise to emulate realistic uncertainty, and each candidate solution is evaluated using a forward–backward sweep load-flow model. Simulation results on the IEEE-69 bus system show that the proposed PSO-RHC scheme reduces total daily energy losses from 1467.50 kWh to 1310.19 kWh (10.72% reduction), improves weakest-bus voltages by 1–4%, and maintains all BESS units within operational limits. The normalized objective components remain small (below 0.5%), indicating balanced operation without excessive cycling. These findings demonstrate the effectiveness and simulation-level effectiveness of PSO-based receding-horizon control for enhancing distribution-network performance under uncertain and dynamic PV conditions. Full article

While traditional microgrid optimization focuses primarily on minimizing Net Present Cost (NPC), real-world deployment success depends on multiple operational factors, including system resilience, capacity headroom, and battery health preservation. This paper presents an enhanced Particle Swarm Optimization (PSO) framework coupled with a Multi-Design Optimization (MDO) methodology that incorporates use-case objective functions that take into account several metrics beyond pure economics. Moreover, the enhanced PSO algorithm allowed us to evaluate near-optimal solutions that can help with plant design with constrained industrial choices. The proposed approach extends the established PSO-MDO method by introducing a customized cost function that simultaneously evaluates: (i) economic viability through NPC, (ii) system resilience through capacity factor and headroom utilization, and (iii) battery longevity through Depth of Discharge (DoD) and C-rate constraints. We apply this methodology to the Areza microgrid in Eritrea, a remote PV-BESS-diesel hybrid system that has been operational since 2019, with two primary objectives: first, quantifying design differences between optimization-derived and as-built configurations, and second, assessing system resilience under load growth scenarios. The results demonstrate that the objective function reveals critical trade-offs invisible to traditional NPC-only optimization, particularly regarding battery stress patterns and system capacity margins. The results reveal a broad near-optimal range where NPC changes by about 5%, but CAPEX varies by almost 28%. This indicates how very different combinations of components can achieve similar lifecycle costs. The best-performing design achieves an NPC ≈ EUR 2.02 M and an LCOE ≈ EUR 0.161/kWh, with sizing around PV 973 kW, battery 2236 kWh, inverter 392 kW, and generator 522 kW. The study highlights key trade-offs: reducing diesel use often leads to more curtailment, and systems that rely more on diesel are more sensitive to demand growth. Smaller systems face higher fuel costs when demand rises by 20%. Full article

Accurate ultra-short-term wind power forecasting is of great significance for grid integration scheduling and the secure operation of power systems. However, due to meteorological disturbances and turbine operating states, wind power series generally exhibit non-stationary, multi-scale fluctuations and strong nonlinearity. To improve forecasting accuracy, this paper proposes an ultra-short-term wind power forecasting model based on a two-stage signal decomposition and a hybrid architecture combining iTransformer, LSTM, and KAN. First, a cascaded decomposition module is constructed using the wavelet transform (WT) and ICEEMDAN to attenuate the non-stationarity of the original power series and to extract multi-scale features. An iTransformer branch is then employed to model global dependencies among multiple variables, while an LSTM branch captures temporal dynamics in the historical power series. Subsequently, a cross-attention mechanism is introduced to achieve cross-branch feature fusion, and a KAN output layer is adopted to enhance the model’s representation of the wind speed–power nonlinear mapping. A particle swarm optimization (PSO) algorithm, combined with a cosine annealing strategy, is used to optimize key hyperparameters and improve training stability. Experimental results using SCADA data from a 150 MW wind farm in southern Hunan Province show that the proposed model achieves an MAE of 9.8327 MW, an RMSE of 13.1872 MW, an SMAPE of 18.8474%, and an $R^2$ of 0.7798. These values correspond to the fixed main comparison protocol used for baseline evaluation, while the ablation study reports multi-seed mean and standard deviation results to assess module-level robustness. Compared with LSTM and WT-ICEEMDAN-CNN-LSTM, the proposed model achieves clear improvements in forecasting accuracy and fitting capability. Additional cross-wind-farm validation on a second wind farm shows that WT-ICEEMDAN-iTransformer-LSTM-KAN-PSO (hereafter referred to as ILKP) maintains the best overall performance, achieving an MAE of 27.2193 MW, an RMSE of 36.1862 MW, an SMAPE of 27.8429%, and an $R^2$ of 0.5189, demonstrating transferability and robustness under different operating conditions. Full article

Collaborative task allocation for multiple unmanned ground vehicles (UGVs) is a constrained combinatorial optimization problem in which symmetric vehicle resources must be coordinated with asymmetric task requirements. In delivery and inspection scenarios, homogeneous vehicles operate under identical rules, whereas task points differ in spatial distribution, demand, service time, and time window requirements. These asymmetries make compact, temporally feasible, and workload-balanced routing difficult. SACWDO-PSO is developed as a discrete particle swarm optimization framework that integrates Clarke–Wright savings initialization, adaptive parameter control, and simulated annealing local search. The savings strategy improves initial swarm quality, adaptive control adjusts exploration and exploitation during the search, and simulated annealing refines local route structures. The method is evaluated on Solomon VRPTW benchmark data under a soft time window penalty objective and insimulation scenarios developed using Unreal Engine 4.27 integrated with Microsoft AirSim 1.8.1. SACWDO-PSO obtains lower objective values and fewer time window violations than the compared swarm-intelligence baselines on most benchmark instances, while Wilcoxon signed-rank tests indicate statistically significant improvements over PSO, DPSO, and GA. Full article

The main motivation for this study is to develop a predictive framework that provides high accuracy at lower computational and experimental costs, resulting in better decision-making in the chosen application domain. Artificial neural networks (ANNs) are widely used for prediction, classification, and pattern recognition tasks. However, their performance is sensitive to the selection of architectural and learning parameters. Hence, an important research challenge is the effective selection of architectural and learning parameters. Several hybrid GA–PSO approaches have been proposed, but most of the existing studies simultaneously optimize network architecture and trainable parameters or focus on a single application domain. However, there is still a lack of systematic framework that optimizes these components separately and validates its performance on multiple heterogeneous datasets. To fill this gap, this study proposes a novel hybrid optimization algorithm, called GAPSO, which combines the genetic algorithm (GA) and particle swarm optimization (PSO) for efficient tuning of artificial neural network (ANN) parameters. The proposed framework is evaluated on five benchmark datasets, including AirPassengers, Sunspots, Death and Injury, Earthquake, and Insurance. In the proposed approach, PSO is used for determination of optimal network architecture (number of hidden neurons) and GA is used for optimization of connection weights and threshold values. The experimental results demonstrate that for four out of five datasets, the lowest MAPE values were achieved by GAPSO-ANN, and were competitive compared to ANN, GA-ANN, PSO-ANN, LSTM and XGBoost models. Additionally, the Wilcoxon signed-rank test showed statistically significant performance improvements (p = 0.03125). Full article

Tower cranes (TCs) as essential lifting equipment in construction engineering, play a critical role in prefabricated buildings (PBs). However, current construction scheduling primarily relies on manual observation and operator experience to execute repetitive tasks, leading to low efficiency, heavy workload, and potential safety risks. In typical PB construction projects, multiple buildings are often constructed in parallel, where each TC is assigned to serve a specific group of buildings independently. This allocation strategy is generally predetermined by the site layout plan to ensure operational safety and avoid inter-crane interference. To enhance lean construction performance and management efficiency in PBs, this study develops a scheduling optimization model that explicitly considers the initial hook position and the specific locations of prefabricated component (PC) supply and demand points. The proposed model is solved and compared using three meta-heuristic algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Artificial Bee Colony (ABC). Numerical results indicate that PSO outperforms GA and ABC in terms of convergence speed and cost minimization performance. After optimization, the operating times of two TCs are reduced by 23.94% and 12.16%, respectively, saving ¥207.29 and ¥293.96 per day in operating costs, and reducing total construction cost by approximately 8.0%. These results demonstrate that the proposed model can effectively improve construction efficiency and support lean management under the considered planning assumptions. Full article