Search Results (48)

Radial distribution networks are highly exposed to sustained service interruptions because faults occurring along upstream feeder sections can affect large groups of downstream customers. This condition motivates the development of cost-effective planning strategies capable of reducing expected energy not supplied (EENS) and improving reliability indices such as the System Average Interruption Frequency Index (SAIFI), System Average Interruption Duration Index (SAIDI), and Customer Average Interruption Duration Index (CAIDI). This study formulates the optimal placement of sectionalizing devices as a binary combinatorial optimization problem in which the objective function minimizes the total expected cost, defined as the sum of customer interruption cost and the annualized investment and installation cost of the selected devices. The formulation considers candidate-branch eligibility, the maximum number of devices, the available investment budget, and the maximum allowable restoration time, while preserving the radial topology of the base feeder by construction. The Aquila Optimizer (AO) is implemented and compared with the Grey Wolf Optimizer (GWO) and a hybrid Genetic Algorithm–Particle Swarm Optimization (GA-PSO) algorithm using the IEEE 69-bus test system under identical population size, iteration budget, number of independent runs, and pseudo-random seed conditions. The results show that the best identified installation of eight sectionalizing devices reduces SAIDI by approximately 58% and the total expected cost by nearly 50% with respect to the base case. SAIFI remains unchanged because the analyzed radial configuration does not include load-transfer paths; therefore, sectionalizing primarily reduces interruption duration rather than interruption frequency. The three algorithms reached solutions of comparable quality around the best identified configuration. GWO exhibited the highest robustness, AO showed a slightly lower computational time than GWO under the adopted MATLAB R2025b-based evaluator, and GA-PSO converged to the same best identified configuration found by GWO. These findings indicate that AO is a competitive computational alternative for sectionalizing-device placement and that a moderate investment in sectionalizing infrastructure can support economically justified reliability improvements in radial distribution networks. Full article

Diagnosing bearing faults remains a crucial challenge, particularly in effectively extracting fault information and achieving high diagnostic accuracy. To address this issue, this study presents a model for diagnosing bearing faults, which comprises three primary stages: feature extraction, feature selection, and classification. In the feature extraction stage, features are extracted from raw motor signals using empirical mode decomposition (EMD) and fast Fourier transform (FFT). In the feature selection stage, an effective method based on binary grey wolf optimization (BGWO) and the equilibrium optimizer (EO) is developed to remove redundant and irrelevant features. Finally, k-nearest neighbours (KNNs) and support vector machine (SVM) classifiers are used to identify bearing fault conditions. The proposed model is evaluated using four datasets: the University of California, Irvine (UCI) benchmark datasets, a motor bearing fault current-signal dataset, the Case Western Reserve University (CWRU) benchmark dataset, and the Machinery Failure Prevention Technology (MFPT) benchmark dataset. The experimental results show that the proposed method improves bearing fault diagnosis accuracy and demonstrates strong robustness compared with conventional methods. Full article

Due to the complexity of recent power grids, any fault can dramatically affect the system’s quality, reliability, and stability. As a result, identifying faults becomes essential to maintaining the stability and reliability of power systems within acceptable thresholds. This article presents an innovative binary classification fault detection method in recent power grids. The proposed methodology primarily consists of two preliminary stages before the training phase: data preparation and pre-training, aimed at improving the performance of the classifier. During the data preparation phase, the synthetic minority over-sampling approach balances the raw data, and the pre-training phase identifies the optimal features and hyperparameters. A novel hybrid approach combines the Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) methods to optimize feature selection and adjust hyperparameters. Furthermore, four machine learning models are trained and evaluated using an actual fault dataset. In addition, several evaluation criteria and receiver operating characteristic curves are used to validate the strength and robustness of the suggested method. All experimental evaluations were performed in an Azure Machine Learning Studio (AMLS) environment. The experimental results are compared to previous studies to verify the superiority of the suggested technique. 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

To address the issues of suboptimal recovery performance, low timeliness, and poor economic efficiency associated with traditional fault recovery methods following large-scale power outages in active distribution networks (ADNs) caused by extreme weather, this paper proposes a dynamic fault recovery strategy for ADNs based on a two-layer hybrid algorithm under extreme ice and snow conditions. First, a line fault rate model considering the thermal effect of current under extreme ice and snow conditions is constructed, and an information entropy-based typical scenario screening method is introduced to filter the fault scenarios. Second, a photovoltaic (PV) output model and a time-varying load model under the influence of extreme ice and snow conditions are established. Subsequently, a multi-objective dynamic fault recovery model is formulated, incorporating island partitioning and integration constraints based on the concept of single-commodity flow, alongside tightened relaxation constraints. To achieve an accurate and rapid solution for the fault recovery model, a two-layer hybrid algorithm is proposed. This algorithm combines an outer-layer improved binary grey wolf optimizer (IBGWO) and an inner-layer second-order cone relaxation (SOCR) algorithm to solve the discrete and continuous decision variables within the model, respectively. Finally, the effectiveness and superiority of the proposed method are verified using the PG&E 69-bus and IEEE 123-bus systems. Full article

In a high-dimensional classification dataset, feature selection is crucial for improving classification performance and computational efficiency by identifying an informative subset of features while reducing noise, redundancy, and overfitting. This study proposes a novel metaheuristic-based ensemble feature selection approach by combining the complementary strengths of Modified Binary Particle Swarm Optimization (MBPSO) and Binary Dynamic Grey Wolf Optimization (BDGWO). The proposed MBPSO–BDGWO ensemble method is specifically designed for high-dimensional classification problems. The performance of the proposed MBPSO–BDGWO ensemble method was rigorously evaluated through an extensive simulation study under multiple high-dimensional scenarios with varying correlation structures. The ensemble method was further validated on several real datasets. Comparative analyses were conducted against single-stage feature selection methods, including BPSO, BGWO, MBPSO, and BDGWO, using evaluation metrics such as accuracy, the F1-score, the true positive rate (TPR), the false positive rate (FPR), the AUC, precision, and the Jaccard stability index. Simulation studies conducted under various dimensionality and correlation scenarios show that the proposed ensemble method achieves a low FPR, a high TPR/Precision/F1/AUC, and strong selection stability, clearly outperforming both classical and advanced single-stage methods, even as dimensionality and collinearity increase. In contrast, single-stage methods typically experience substantial performance degradation in high-correlation and high-dimensional settings, particularly BPSO and BGWO. Moreover, on the real datasets, the ensemble method outperformed all compared single-stage methods and produced consistently low MAD values across repetitions, indicating robustness and stability even in ultra-high-dimensional genomic datasets. Overall, the findings indicate that the proposed ensemble method demonstrates consistent performance across the evaluated scenarios and achieves higher selection stability compared with the single-stage methods. Full article

This study optimizes the placement of μ-PMUs using the BPSO and BGWO algorithms for the IEEE 33-bus and 69-bus systems, with a focus on minimizing deployment costs while ensuring robust system observability. Three case studies are analysed: Case 1 (normal conditions), Case 2 (single μ-PMU outage), and Case 3 (Zero Injection Buses, ZIBs). In Case 1, both algorithms identified 24 μ-PMUs as the optimal placement for the IEEE 69-bus system, achieving the minimum PMUs required for full observability. For Case 2, redundancy requirements increased the μ-PMU count to 24 μ-PMUs for the IEEE 33-bus system and 51 μ-PMUs for the IEEE 69-bus system, ensuring full observability even under a single μ-PMU failure. Case 3, leveraging Zero Injection Buses (ZIBs), reduced the μ-PMU count to 20 μ-PMUs for both BPSO and BGWO, optimizing the system configuration while maintaining observability. A trade-off analysis was performed to examine the trade-off between redundancy and PMU count, showing that increasing the number of μ-PMUs improves system resilience. Voltage and current channels were measured from the optimized placements to ensure accurate voltage measurement in all case studies. Subsequently, the Weighted Least Squares algorithm was applied for voltage estimation, serving as a peripheral to the main objective of the optimal μ-PMU placement. Voltage estimation was conducted under three noise levels: 0.01 STD for basic analysis and 0.02 and 0.04 STD to observe the impact of varying measurement noise. The results highlight that higher μ-PMU placements improve voltage estimation accuracy, particularly under higher noise levels. Statistical analysis confirms that BGWO outperforms BPSO in terms of computational efficiency, stability, and convergence, especially in large-scale systems. By enhancing grid monitoring and state estimation, this research directly contributes to the development of more resilient and efficient power networks, which is a fundamental prerequisite for integrating renewable energy sources and advancing overall power system sustainability. This research emphasizes the balance between cost and reliability in μ-PMU placement and provides a comprehensive methodology for state estimation in modern power systems. Full article

Wireless sensor networks (WSNs) represent an emerging technology, among which coverage optimization remains a fundamental challenge. In specific application scenarios such as intelligent urban management, three-dimensional (3D) coverage models better reflect real-world requirements and thus hold greater research significance. To maximize the coverage performance of 3DWSNs, this study proposes a Three-Dimensional Confident Information Coverage (3DCIC) model based on the concept of multi-node cooperative information reconstruction, effectively extending the perceptual domain of sensor nodes. Furthermore, by incorporating adaptive dimension learning and opposition-based learning metchanisms into the wolf pack update strategy, we have developed the Hopping Adaptive Grey Wolf Optimizer (HAGWO) based on the GWO to optimize node deployment. Experimental results demonstrate the superior performance of the 3DCIC model, achieving coverage ranges 2.78 times, 4.41 times, and 4.00 times greater than those of conventional binary spherical models under regular tetrahedral, hexahedral, and octahedral node deployments, respectively. The proposed scheduling algorithm proves highly effective in both classical test functions and three-dimensional coverage problems. Full article

Defect-rate uncertainty creates cascading operational challenges in multi-stage production, often driving inefficiency and misallocation of labor, materials, and capacity. To confront this, we develop a multi-stage Production Integrated Decision (MsPID) framework that unifies quality inspection and shop-floor decision-making within a single computational model. The framework couples a two-stage sampling inspection policy—used to statistically learn and control defect-rate uncertainty via estimation and rejection rules—with a multi-process, multi-part production decision model. Optimization is carried out with an Improved Grey Wolf Optimizer (IGWO) enhanced with Latin hypercube sampling (LHS) for uniformly diverse initialization; an evolutionary factor mechanism that blends simulated binary crossover (SBX) among three leadership-guided parents (Alpha, Beta, Delta) to strengthen global exploration in early iterations and focus exploitation later; and a greedy, mutation-assisted opposition learning step applied to the lowest-performing quartile of the population to effect leader-informed local refinement and accept only fitness-improving moves. Experiments show the method identifies minimum-cost policies across six single-stage benchmark cases and yields a total profit of 43,800 units in a representative multi-stage scenario, demonstrating strong performance in uncertain environments. Sensitivity analysis further clarifies how recommended decisions adapt to shifts in estimated defect rates, finished product prices, and swap/changeover losses. These results highlight how bio-inspired intelligence can enable adaptive, efficient, and resilient integrated production management at scale. Full article

Binarizing continuous metaheuristics to solve challenging NP-hard binary optimization problems is a fundamental step in adapting continuous algorithms for discrete domains. Binary optimization problems, such as the Set Covering Problem and the 0–1 Knapsack Problem, demand tailored approaches to efficiently explore and exploit the solution space. The process of binarization often introduces complexities, as it requires balancing the transformation of continuous populations into binary solutions while preserving the algorithm’s capability to navigate the search space effectively. In this context, we explore the performance of the Reptile Search Algorithm (RSA), a continuous metaheuristic, applied to these two benchmark problems. To address the binary nature of the problems, a two-step binarization process is implemented, utilizing combinations of transfer functions with binarization rules. This framework enables the RSA to generate binary solutions while leveraging its inherent strengths in exploration and exploitation. Comparative experiments are conducted with Particle Swarm Optimization and the Grey Wolf Optimizer to benchmark the RSA’s performance under similar conditions. These experiments analyze critical factors such as fitness values, convergence behavior, and exploration–exploitation dynamics, providing insights into the effectiveness of different binarization approaches. The results demonstrate that the RSA achieves competitive performance across both problems, highlighting its flexibility and adaptability, which are attributed to its diverse movement equations. Notably, the Z4 transfer function consistently enhances performance for all algorithms, even when paired with less effective binarization rules. This indicates the potential of Z4 as a robust transfer function for binary optimization. The findings underscore the importance of selecting appropriate binarization strategies to maximize the performance of continuous metaheuristics in binary domains, paving the way for further advancements in hybrid optimization methodologies. Full article

Microgrids play a crucial role in the development of future smart grids, with multiple interconnected microgrids forming large-scale multi-microgrid systems that operate as smart grids. Multi-agent system (MAS)-based control solutions are the most suitable for addressing such control challenges. This paper presents a demand-side management (DSM) strategy using a meta-heuristic optimization technique for minimizing the household energy consumption cost using MAS. The binary grey wolf optimization algorithm (BGWOA) optimizes load scheduling, reducing electricity costs, without compromising consumer preferences using time-of-day (ToD) tariffs. The communication agents and load agents comprise the MAS used to streamline load control operations. The results demonstrate that MAS-based load control using metaheuristic optimization techniques enhances demand-side management, thus minimizing the electricity costs while adhering to contradictory parameters like user preferences, appliance duration, and load atomicity. This makes renewable energy integration more cost-effective in smart grids, thereby ensuring affordable, reliable, and sustainable energy for all. Full article

The Set Coverage Problem (SCP) is an important combinatorial optimization problem known to be NP-complete. The use of metaheuristics to solve the SCP includes different algorithms. In particular, binarization techniques have been explored to adapt metaheuristics designed for continuous optimization problems to the binary domain of the SCP. In this work, we present a new approach to solve the SCP based on the Secretary Bird Optimization Algorithm (SBOA). This algorithm is inspired by the natural behavior of the secretary bird, known for its ability to hunt prey and evade predators in its environment. Since the SBOA was originally designed for optimization problems in continuous space and the SCP is a binary problem, this paper proposes the implementation of several binarization techniques to adapt the algorithm to the discrete domain. These techniques include eight transfer functions and five different discretization methods. Taken together, these combinations create multiple SBOA adaptations that effectively balance exploration and exploitation, promoting an adequate distribution in the search space. Experimental results applied to the SCP together with its variant Unicost SCP and compared to Grey Wolf Optimizer and Particle Swarm Optimization suggest that the binary version of SBOA is a robust algorithm capable of producing high quality solutions with low computational cost. Given the promising results obtained, it is proposed as future work to focus on complex and large-scale problems as well as to optimize their performance in terms of time and accuracy. Full article

Node localization is a critical challenge in Internet of Things (IoT) applications. The DV-Hop algorithm, which relies on hop counts for localization, assumes that network nodes are uniformly distributed. It estimates actual distances between nodes based on the number of hops. However, in practical IoT networks, node distribution is often non-uniform, leading to complex and irregular topologies that significantly reduce the localization accuracy of the original DV-Hop algorithm. To improve localization performance in non-uniform topologies, we propose an enhanced DV-Hop algorithm using Grey Wolf Optimization (GWO). First, the impact of non-uniform node distribution on hop count and average hop distance is analyzed. A binary Grey Wolf Optimization algorithm (BGWO) is then applied to develop an optimal anchor node selection strategy. This strategy eliminates anchor nodes with high estimation errors and selects a subset of high-quality anchors to improve the localization of unknown nodes. Second, in the multilateration stage, the traditional least square method is replaced by a continuous GWO algorithm to solve the distance equations with higher precision. Simulated experimental results show that the proposed GWO-enhanced DV-Hop algorithm significantly improves localization accuracy in non-uniform topologies. Full article

Breast cancer is the most common disease in women, with 287,800 new cases and 43,200 deaths in 2022 across United States. Early mammographic picture analysis and processing reduce mortality and enable efficient treatment. Several deep-learning-based mammography classification methods have been developed. Due to low-contrast images and irrelevant information in publicly available breast cancer datasets, existing models generally perform poorly. Pre-trained convolutional neural network models trained on generic datasets tend to extract irrelevant features when applied to domain-specific classification tasks, highlighting the need for a feature selection mechanism to transform high-dimensional data into a more discriminative feature space. This work introduces an innovative and effective multi-step pathway to overcome these restrictions. In preprocessing, mammographic pictures are haze-reduced using adaptive transformation, normalized using a cropping algorithm, and balanced using rotation, flipping, and noise addition. A 32-layer convolutional neural model inspired by YOLO, U-Net, and ResNet is intended to extract highly discriminative features for breast cancer classification. A modified Grey Wolf Optimization algorithm with three significant adjustments improves feature selection and redundancy removal over the previous approach. The robustness and efficacy of the proposed model in the classification of breast cancer were validated by its consistently high performance across multiple benchmark mammogram datasets. The model’s constant and better performance proves its robust generalization, giving it a powerful solution for binary and multiclass breast cancer classification. Full article

Diabetes requires effective monitoring of the blood glucose level (BGL), traditionally achieved through invasive methods. This study addresses the non-invasive estimation of BGL by utilizing heart rate variability (HRV) features extracted from photoplethysmography (PPG) signals. A systematic feature selection methodology was developed employing advanced metaheuristic algorithms, specifically the Improved Dragonfly Algorithm (IDA), Binary Grey Wolf Optimizer (bGWO), Binary Harris Hawks Optimizer (BHHO), and Genetic Algorithm (GA). These algorithms were integrated with machine learning (ML) models, including Random Forest (RF), Extra Trees Regressor (ETR), and Light Gradient Boosting Machine (LightGBM), to enhance predictive accuracy and optimize feature selection. The IDA-LightGBM combination exhibited superior performance, achieving a mean absolute error (MAE) of 13.17 mg/dL, a root mean square error (RMSE) of 15.36 mg/dL, and 94.74% of predictions falling within the clinically acceptable Clarke error grid (CEG) zone A, with none in dangerous zones. This research underscores the efficiency of utilizing HRV and PPG for non-invasive glucose monitoring, demonstrating the effectiveness of integrating metaheuristic and ML approaches for enhanced diabetes monitoring. Full article