Search Results (951)

To address the inverse identification of contact-related thermal faults in gas-insulated switchgear (GIS), this study proposes a method for contact resistance inversion and internal temperature field reconstruction. The proposed method enables the estimation of faulty internal contact resistance using external enclosure temperature data, while simultaneously reconstructing the internal temperature field. First, a forward numerical model of GIS is established, and a POD-Kriging surrogate model is developed to achieve second-level rapid prediction of the forward problem. Based on this surrogate model, the thermal fault inversion problem is formulated as an optimization problem of fault parameters and solved using the Grey Wolf Optimizer. GIS temperature-rise experiments are performed to validate the numerical model, and a real GIS contact fault case is further analyzed. The results indicate that the proposed method yields an average inversion error of 9.5% for degraded contact resistance, with the maximum error at internal temperature monitoring points remaining below 8%. The total inversion time is approximately 30 s. These findings demonstrate that the proposed method is capable of effective online inversion and diagnosis of contact-related thermal faults in GIS equipment. Full article

Integrated Gate-Commutated Thyristor (IGCT) serves as the core power electronic device in high-voltage and high-power renewable energy conversion systems. Aiming at the problems of slow convergence, easy to fall into local optima, and difficulty in balancing multi-objective performance in traditional IGCT gate drive design under power fluctuation conditions, this paper proposes an IGCT gate drive optimization method based on the Improved Grey Wolf Optimization (IGWO) algorithm. A multi-objective optimization model is established with switching loss reduction, voltage overshoot suppression, current oscillation attenuation and driving capability guarantee as objectives and gate resistance and driving voltage as optimization variables. The traditional grey wolf algorithm is improved by adaptive weight adjustment and dynamic search step strategies to balance global exploration and local exploitation. Simulation and experimental results show that, compared with the traditional Grey Wolf Algorithm (GWO) and Particle Swarm Optimization (PSO), the convergence speed of IGWO is increased by 40.4% and 51.0%, and the optimization accuracy is improved by 12.7% and 18.1%, respectively. Compared with the conventional empirical design, the optimized drive circuit reduces the switching loss by 31.8%, suppresses the voltage overshoot by 33.7%, decreases the current oscillation by 38.6%, and shortens the driving rise time by 39.3%. The proposed method realizes the automatic and precise tuning of IGCT gate drive parameters, effectively improves the switching performance and operation stability of IGCT under renewable energy fluctuation conditions, and provides a practical intelligent optimization scheme for the high-performance gate drive design of high-power IGCT devices. Full article

This paper proposes a new fuzzified multi-objective wireless communication optimization model that maximizes the quantity and placement of Reconfigurable Intelligent Surfaces (RISs). In order to meet realistic deployment constraints like non-overlapping and acceptable location, the model aims to decrease the number of deployed RISs while raising the achievable rate. The Modified Pied Kingfisher Optimization Algorithm (MPKOA) is suggested as a solution to this intricate optimization issue. MPKOA features many significant improvements over the traditional Pied Kingfisher Optimization Algorithm (PKOA), such as energy-based motion control, adaptive subgrouping, flock cooperation, and memory-driven re-perching. These techniques speed up convergence, improve solution precision, reduce computation time, and balance exploration and exploitation. MPKOA performs better than standard PKOA, Enhanced version of PKOA (EPKO), Differential Evolution (DE), Grey Wolf Optimizer (GWO), and other existing algorithms, according to extensive comparisons. MPKOA can achieve up to 20% higher optimization values and 30% faster convergence, according to simulation data. In addition, the proposed MPKOA reduces computational complexity and runtime by about 50% when compared to standard PKOA-based approaches since it only requires single fitness evaluation per iteration. This enables the deployment of fewer RISs while still achieving higher communication rates. In multiuser wireless systems, MPKOA offers a robust and effective approach to RIS placement optimization, which helps to boost capacity and provide more energy-efficient 6G communication networks. Full article

This paper presents an efficient inverse kinematics (IK) solution for robotic manipulators, addressing the challenges of high computational complexity, low efficiency, and sensitivity to singularities associated with traditional methods. A data augmentation strategy is introduced, utilizing an enhanced Diffusion-TS model to generate diverse joint-angle samples and corresponding end-effector poses through forward kinematics, thereby creating a high-quality dataset. To improve real-time performance, a Temporal Convolutional Network (TCN) model is developed, optimized using the Grey Wolf Optimizer (GWO), and augmented with a probabilistic sparse attention mechanism to effectively capture key pose features. Experimental evaluations on the Jaka MiniCobo robotic arm demonstrate that the proposed method significantly reduces inference time while maintaining high accuracy, making it suitable for real-world applications that demand both speed and precision. Full article

Large-scale smart city Internet of Things (IoT) infrastructures must simultaneously provide strong cybersecurity protection, real-time anomaly detection, and energy-efficient operation despite the strict resource limitations of sensing devices. The current body of research typically addresses secure data management, edge intelligence, or energy optimization in isolation, leaving a practical gap in unified frameworks that jointly optimize these objectives. This paper proposes a jointly co-designed energy-aware cybersecurity framework that integrates lightweight secure sensing, hybrid edge-based anomaly detection, Practical Byzantine Fault Tolerance (PBFT)-enabled blockchain integrity, and Grey Wolf Optimization (GWO)-driven edge deployment within a single end-to-end architecture. The practical contribution of the proposed framework lies in enabling tamper-evident trusted sensing, real-time detection of both data and energy anomalies, and communication-efficient operation suitable for scalable smart city deployments. The simulation results demonstrate that the proposed method achieves strong operational efficiency, reaching up to 234.6 transactions per second while maintaining end-to-end latency of approximately 140–194 ms and reducing total energy consumption to about 1.68 J under high-load conditions. In addition, the hybrid anomaly detection mechanism achieves an F1-score of 0.985 and ROC-AUC of 0.992, confirming strong detection capability under realistic sensing and attack scenarios. Full article

Runoff time series often exhibit nonlinear and fluctuating characteristics, and their complexity has further increased with the intensification of global climate change; high-precision daily-scale forecasting remains a core challenge in the field of hydrological forecasting. Addressing the shortcomings of existing methods in terms of runoff feature extraction capabilities and limited forecasting accuracy, this paper aims to improve the accuracy of daily runoff forecasting in small watersheds by constructing a hybrid forecasting model that integrates optimization algorithms, signal decomposition, and deep learning models. Specifically, the original runoff data is first preliminarily decomposed using a variational mode decomposition (VMD) method optimized by the grey wolf optimization (GWO) algorithm. The mode components obtained from the decomposition are evaluated using Fuzzy Entropy (FE), and the selected high-entropy components (IMFs) are then input into a second-order decomposition using an optimized Wavelet Transform (WT) to further extract latent features. After decomposition, the mode components are reassembled; second, a bidirectional long short-term memory (BiLSTM) model for daily runoff prediction is constructed for each subcomponent, and the model’s hyperparameters are optimized using an optimization algorithm; finally, the prediction results are reconstructed to obtain the final output. Case studies were conducted using three hydrological stations—Nanfeng, Baiquan, and Shaziling—in the Xujiang River basin of the Fuhe River. The experimental results indicate that by incorporating an optimization mechanism and a two-stage decomposition strategy, the proposed model achieved an NSE of over 0.95 at all three stations. Compared to the baseline BiLSTM model, the proposed model reduced the RMSE by 76.69%, 75.82%, and 65.92% at the three stations, respectively, and reduced the MAE by 64.77%, 73.54%, and 50.46%, and NSE increased by 27.82%, 40.06%, and 38.02%, respectively. This demonstrates that the model exhibits excellent reliability and superiority in daily-scale runoff forecasting for small watersheds. Full article

This paper proposes a dynamic obstacle-avoidance algorithm for unmanned surface vehicles (USVs) that combines a Fuzzy-enhanced Dynamic Window Approach (FDWA) with an Improved Bidirectional A*–Improved Grey Wolf Optimizer (IBA*–IGWO) framework. Firstly, the traditional dynamic window method (DWA) is improved by adopting an initial heading angle optimization strategy to reduce the heading deviation of unmanned vessels during cruising. Secondly, a fuzzy controller is introduced, which can adaptively adjust the weight coefficients in the cost function of the DWA algorithm based on the current position of the unmanned vessel, surrounding environmental information, etc., to improve obstacle avoidance ability and adaptability in different environments. Finally, using the global static cruise route provided by the IBA*–IGWO algorithm, key nodes are selected as local endpoints for the FDWA algorithm to ensure that the unmanned vessel can perform cruise tasks according to the optimal plan during navigation and make dynamic adjustments in case of emergencies. The simulation results demonstrate the feasibility of the proposed method in handling unknown and dynamic obstacles under the current grid-based experimental settings, while enabling the USV to return to the pre-planned global route after local obstacle avoidance. These results provide a basis for further development toward more robust and rule-aware autonomous navigation in realistic maritime environments. Full article

Yarn tenacity is one of the vital quality parameters that determine the performance, fabric durability and end use suitability. The tenacity of yarn is largely influenced by the fibre characteristics used. The physical properties of jute fibres, including root content, defect, bundle strength, and fineness, exert a significant influence on yarn tenacity. This study utilized metaheuristic optimized random forest regression (RFR) to predict jute yarn tenacity from fibre parameters. The hyperparameters of the RFR models were optimized using four metaheuristic algorithms: whale optimization algorithm (WOA), grey wolf optimization (GWO), beetle antennae search (BAS) and ant colony optimization (ACO). The model utilized a dataset comprising 414 experimental data with 70% data for training and 30% for testing the model, using input variables such as bundle strength (g/tex), defects (%), root content (%) and fineness (tex) to predict yarn tenacity (cN/tex). The developed models effectively predicted yarn tenacity. However, RFR–GWO achieved slightly better performance with R² of 1.0 for training set and 0.96 for test set. Regarding execution time, RFR–GWO is the fastest requiring only 14.25 s. SHAP analysis revealed that bundle strength and root content of jute fibre are the most influential factors, whereas defect and fineness exert the least influence on model’s prediction. The best model RFR–GWO was deployed into an interactive Streamlit web application, offering an intuitive and user-friendly platform for the real-time estimation of yarn tenacity. Full article

The next generations of wireless networks will use more intensively shared spectrum and hardware resources. This leads to huge demand for integrated sensing and communication (ISAC) technology. Additionally, the integration of millimeter-wave (mmWave) spectrum can improve the sensing capabilities and communication rates of ISAC systems. This development is of great significance to the internet of things (IoT), as it is essential for intelligent operations and decision-making to have accurate surround sensing and device communication. This study presents a novel methodology for beamforming design in mmWave ISAC base stations within IoT systems, utilizing a grey wolf optimizer (GWO) to optimize the total communication rate and effective sensing power. Also, this work is mostly focused on simulation and heuristic optimization methods. The analyses conducted indicate that the suggested GWO-based optimization achieves a sum rate of up to 22.7 $\mathrm{bit}/\mathrm{s}/\mathrm{Hz}$ and a sensing power of 65.8 dBm when the base station (BS) is equipped with 8 antennas, in comparison to the results from the particle swarm optimization (PSO)-based and genetic algorithm (GA)-based schemes. Full article

Compact 5G millimeter-wave (mm-Wave) multiple-input multiple-output (MIMO) systems face a serious challenge as high isolation is required for high spectral efficiency. This paper presents a novel computational design framework for enhancing the isolation of a two-port ultra-wideband (UWB) MIMO antenna, specifically targeting the 5G n257 band (26.5–29.5 GHz). A pixelated metasurface is presented and optimized with the help of a binary-coded Grey Wolf Optimizer (B-GWO) algorithm through a MATLAB-Computer Simulation Technology (CST) co-simulation interface, which is used in contrast to some conventional decoupling structures. A Geometric Mirror Symmetry method is used to accelerate the optimization process, which halves the number of optimization variables and significantly reduces the computational load. Crucially, this symmetry is also a fundamental requirement to ensure that the reflection coefficients ($S_{11}$, $S_{22}$) of the antennas remain identical. The proposed design achieves isolation levels better than 20 dB across the entire target band, reaching a peak isolation of $32.58$ dB at $28.67$ GHz, while maintaining reflection coefficients ($S_{11}$, $S_{22}$) below $-10$ dB. The MIMO diversity performance is comprehensively validated with an Envelope Correlation Coefficient (ECC) $<0.005$, a Diversity Gain (DG) of $9.99$ dB, and a Total Active Reflection Coefficient (TARC) $<-10$ dB. Moreover, the suppression of surface waves enhances the realized gain to $4.51$ dBi, providing a $0.57$ dB improvement over the reference antenna. In addition, an equivalent passive RLC circuit model is constructed to observe the physical process of the pixelated surface, which shows the optimized structure as a band stop filter at the coupling frequency. The high correlation of the Equivalent Circuit Model and full-wave simulation outcomes confirms that the suggested design procedure is a strong verification alternative to physical fabrication. Full article

This paper addresses the NP-hard 2D irregular packing problem with non-convex geometric constraints. We propose a distributed hybrid metaheuristic based on an island population structure, integrating a genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing (SA), and a grey wolf optimizer (GWO), with a novel Modular Adaptive Optimization Module (MAOM). The passivity and stability of the MAOM are rigorously proven via a Lyapunov energy function. The convergence rate of the island model is proven to be $O(T_{\max }/K)$, demonstrating linear speedup. Extensive experiments on 11 benchmark datasets show that the proposed algorithm achieves material utilization ranging from 61.73% to 79.42% with excellent stability ($\mathrm{CV}<0.03$). Statistical tests confirm significant improvements over traditional metaheuristics ($p<0.05$). This work provides a theoretically grounded and practically effective approach for 2D irregular nesting. Full article

Accurate estimation of reference evapotranspiration (ET₀) can be decisive in agricultural, hydrological and meteorological applications. Although different machine learning (ML)-based models have been successfully applied for ET₀ estimation under a wide spectrum of climatic conditions, most of these models present the crucial shortcoming of being site-specific. Hence, a thorough hyperparameter tuning would be necessary before translating such models to another domain with different data distributions. The hyperparameter tuning is a complex procedure that mainly depends on the operator’s experience. Automated ML might be a suitable approach to adapt the models’ architectures. The present study evaluated the performance of different automated ML algorithms, namely, neural architecture search (NAS), Optuna, enhanced grey wolf (EGWO), and quantum whale optimization (QWOA) algorithms coupled with random forest, neural networks, and light gradient boosting models for estimating daily ET₀ at three different climatic regions (Cairo, Singapore, and London). For local validation, the NN-NAS model provided the most accurate results in Cairo (R² = 0.969, RMSE = 0.432 mm/day) and Singapore (R² = 0.657, RMSE = 0.596 mm/day), while NN-Optuna provided the highest performance accuracy in London (R² = 0.941, RMSE = 0.370 mm/day). Hybrid AutoML models improved R² by 5–15% and reduced RMSE by 10–20% compared to standalone models. In external validation, NN-NAS and NN-Optuna presented superior generalizability, with R² values up to 0.899 and 0.680 in London and Cairo, respectively. Nonetheless, the performance of the hybrid models depended on the climatic conditions of the studied sites, where NN-NAS was the best model for the arid site, while NN-Optuna provided the highest accuracy in the temperate climate. Further, the analysis of variance confirmed significant differences among the performance accuracies of the developed model. The Shapley additive explanations (SHAP) analysis was performed to identify the variables’ effect on ET₀ estimation, which suggested that solar radiation showed the highest impact in all three studied climatic contexts, although the degree of importance was climatic dependent. Finally, an external modeling scenario was conducted using exogenous data for estimating ET₀ at the target sites, which confirmed the models’ ability. Full article

The rising computational demands of deep learning models have intensified concerns regarding their energy consumption and environmental impact, motivating the development of Green Artificial Intelligence (Green AI) approaches. This paper proposes a multi-objective Green AI optimization framework based on the Grey Wolf Optimizer (GWO) to design efficient multilayer perceptron (MLP) architectures. Unlike conventional strategies that focus solely on maximizing accuracy, the proposed method jointly optimizes validation accuracy, training time, number of trainable parameters, and estimated floating-point operations (FLOPs). Evaluated on the Fashion-MNIST dataset and compared against a baseline MLP and Random Search, the GWO-based approach achieves competitive predictive performance while drastically reducing model size, computational complexity, and training time. Pareto front analysis confirms that GWO consistently identifies non-dominated architectures that offer superior trade-offs between accuracy and efficiency. Additional equal-accuracy evaluations demonstrate improved convergence efficiency and stability despite reduced model complexity. The results provide empirical evidence, within the MLP design setting considered in this study, that bio-inspired multi-objective optimization can support Green AI by identifying more compact and efficient architectures with competitive predictive performance. Full article

Accurate prediction of reference evapotranspiration (ET₀) using integrated deep learning approaches with limited meteorological data is highly significant for efficient water resource utilization and management in arid regions. Nevertheless, parameter optimization is frequently overlooked in current research, leading to unsatisfactory estimation accuracy that cannot meet practical application requirements. To overcome this limitation, a CNN–BiLSTM–attention hybrid model is constructed by combining the powerful feature-extraction capability of CNN and excellent sequence-processing performance of BiLSTM, followed by the integration of an attention mechanism. Five metaheuristic algorithms, namely the osprey optimization algorithm (OOA), grey wolf optimization (GWO), whale optimization algorithm (WOA), particle swarm optimization (PSO), and northern goshawk optimization (NGO), are adopted to optimize the key parameters of the proposed model. The developed hybrid models are then applied to ET₀ estimation in Linze County, China. The results demonstrate that the error indices of these models vary within the ranges of MAPE [14.28%, 14.48%], MAE [0.4270, 0.4482], RMSE [0.5596, 0.5844], and NMSE [0.0490, 0.0577]. Overall, the OOA–CNN–BiLSTM–attention model exhibited the most robust and consistent estimation performance across multiple evaluation metrics among the investigated models. Full article

This study presents a comprehensive multi-objective optimization of sewage wastewater treatment using bio-based coagulants, guided by the Grey Wolf Optimizer (GWO) and its multi-objective variant (MOGWO). Experimental coagulation data, employing Citrullus lanatus and Cucumis melo as natural coagulants, were modeled using multivariate regression techniques, yielding high coefficients of determination (R² > 0.95) across key water quality parameters. The optimization process targeted maximal reductions in turbidity, total suspended solids (TSS), biochemical oxygen demand (BOD), and chemical oxygen demand (COD) through strategic manipulation of pH and coagulant dosage. The single-objective GWO achieved significant outcomes, including a 96.68% turbidity reduction at pH 5 and 50 mg/L dosage. The MOGWO algorithm identified Pareto-optimal solutions, such as a 94.2% turbidity reduction at pH 5 and 72 mg/L dosage, and a balanced BOD reduction of 52.7% at pH 7. The predictive models indicated that optimal treatment conditions could reduce chemical usage by up to 90% compared to conventional coagulants, resulting in potential cost savings of up to 30%. Moreover, the algorithms demonstrated rapid convergence, averaging 200 iterations, highlighting their computational efficiency and robustness. These findings illustrate that integrating bio-based coagulants with advanced optimization techniques can achieve high treatment efficiency while reducing chemical inputs, thus directly supporting environmental sustainability by minimizing sludge and secondary pollution. In this situation, the wastewater treatment plant will focus on resource-recovery systems with less or no waste at the end of the treatment process. This approach aligns with circular economy principles by promoting eco-friendly, cost-effective wastewater treatment solutions suitable for resource-limited settings. The study offers a forward-looking pathway for environmentally responsible wastewater management practices that significantly reduce chemical dependency and contribute to pollution mitigation efforts. Full article