Grey Wolf Optimization Algorithm Research Articles

Prediction of flow and dynamic recrystallization behavior based on three machine learning methods for a novel duplex-phase titanium alloy

In this work, the vacuum arc-melting was used to prepare the Ti-10V-5Al-2.5Fe-0.1B alloy. Single-pass isothermal compression experiments were carried out on the alloy in the temperature range of 770-920 °C at strain rates of 0.0005-0.5 s-1. The BP model optimized by the bald eagle search algorithm (BES-BP), the BP model optimized by the sparrow search algorithm (SSA-BP), and the BP model optimized by the gray wolf optimization algorithm (GWO-BP) were developed for high-precision prediction of flow stress. The above models were compared by using the mean square correlation coefficient, root mean square error, and average absolute relative error between the predicted and experimental flow stress. The three prediction accuracy parameters have indicated that the BES-BP model has a higher accuracy for flow stress prediction at the known and the new process parameters. A hot processing map based on the dynamic materials model was developed by using the flow stress predicted in the framework of the BES-BP model, and EBSD analysis was performed as well. The results show that the degree of dynamic recrystallization increases with an increase in the power dissipation factor, and the formation of deformation bands is the main cause of instability. The minimum critical stress for inducing dynamic recrystallization of the alloy was found to be 13.13 MPa at 890 °C/0.0005 s-1. Moreover, the power dissipation factor increases with a decrease in critical stress. In addition, microstructure validation data reveal that the dynamic recrystallization model has a high accuracy for critical stress prediction, confirming that the critical stress increases with a decrease in the dynamic recrystallization fraction.

An altitude-aware fuzzy approach for energy efficiency in UAV-assisted 3D Wireless Sensor Networks

In Wireless Sensor Networks (WSNs) that use multi-hop topologies, issues like energy holes and hotspots have become prominent. To address these, recent research has proposed using mobile sinks with abundant resources. These include mobile robots, drones, and notably, Unmanned Aerial Vehicles (UAVs), as solutions to alleviate these challenges. This paper introduces a novel altitude-aware fuzzy approach aimed at improving energy efficiency in UAV-supported 3D WSNs. The proposed methodology comprises two key components. Firstly, a tailored fuzzy clustering algorithm is developed to manage the spatial structure of the 3D WSN, optimizing energy consumption. Secondly, a hybrid grey wolf optimization algorithm is utilized to fine-tune the parameters of the fuzzy clustering algorithm, ensuring optimal performance. The synergistic and seamless integration of these components addresses the energy efficiency challenges inherent in UAV-assisted 3D WSNs. The significance of this approach lies in its capacity to navigate the escalating complexity and energy demands of modern sensor networks, offering a harmonious blend of theoretical innovation and practical applicability. Experimental analysis and results substantiate the superior performance of the proposed approach compared to existing solutions, as measured by the metrics commonly employed to evaluate the network lifetime of protocols in the literature.

Classification of Dog Breeds Using Convolutional Neural Network Models and Support Vector Machine

When classifying breeds of dogs, the accuracy of classification significantly affects breed identification and dog research. Using images to classify dog breeds can improve classification efficiency; however, it is increasingly challenging due to the diversities and similarities among dog breeds. Traditional image classification methods primarily rely on extracting simple geometric features, while current convolutional neural networks (CNNs) are capable of learning high-level semantic features. However, the diversity of dog breeds continues to pose a challenge to classification accuracy. To address this, we developed a model that integrates multiple CNNs with a machine learning method, significantly improving the accuracy of dog images classification. We used the Stanford Dog Dataset, combined image features from four CNN models, filtered the features using principal component analysis (PCA) and gray wolf optimization algorithm (GWO), and then classified the features with support vector machine (SVM). The classification accuracy rate reached 95.24% for 120 breeds and 99.34% for 76 selected breeds, respectively, demonstrating a significant improvement over existing methods using the same Stanford Dog Dataset. It is expected that our proposed method will further serve as a fundamental framework for the accurate classification of a wider range of species.

Numerical Prediction of Solid Particle Erosion in Jet Pumps Based on a Calibrated Model

Jet pumps are widely used in petrochemical processes, nuclear cooling, and wastewater treatment due to their simple structure, high reliability, and stable performance under extreme conditions. However, when transporting solid-laden two-phase flows, they face severe erosion problems, leading to reduced efficiency, malfunctions, or even failure. Therefore, optimizing jet pump performance and extending its service life is crucial. In this study, an experimental platform was established to conduct experiments on wall erosion in jet pumps. The CFD-DEM method was used to simulate the solid–liquid two-phase flow in the jet pump, comparing six erosion models for predicting erosion rates. The Grey Wolf Optimization algorithm was applied to calibrate model coefficients. The results indicate that the Neilson erosion model shows the best consistency with the experimental results. The inlet flow rate significantly influenced the erosion rates, while the flow rate ratio had a smaller effect. The particle concentration exhibited a nonlinear relationship with erosion, with diminishing impact beyond a certain threshold. As the factors varied, the erosion distribution tended to be uniform, but high erosion areas remained locally concentrated, indicating intensified localized erosion.

Multi-Level Thresholding Color Image Segmentation Using Modified Gray Wolf Optimizer

The success of image segmentation is mainly dependent on the optimal choice of thresholds. Compared to bi-level thresholding, multi-level thresholding is a more time-consuming process, so this paper utilizes the gray wolf optimizer (GWO) algorithm to address this issue and enhance accuracy. To acquire the optimal thresholds at different levels, we modify the GWO (MGWO) in terms of leader selection, position update, and mutation. We also use the Otsu method and Kapur entropy as objective functions. The performance of MGWO is compared with other color image segmentation algorithms on ten images from the BSD500 dataset in terms of objective values, variance, signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and feature similarity index (FSIM). Experimental and non-parametric statistical analyses demonstrate that MGWO performs excellently in the multi-level thresholding segmentation of color images.

Research on photovoltaic MPPT under complex conditions based on an advanced great wall construction algorithm method.

To mitigate the challenges posed by the non-linear multi-peak power-voltage output characteristics of photovoltaic (PV) systems operating under partial shading conditions, which often lead to suboptimal performance of conventional Maximum Power Point Tracking (MPPT) algorithms, a novel approach was introduced. We introduce the LGWGCA-P&O method, which synergistically combines an modified Great Wall Construction Algorithm (LGWGCA) with the Perturbation and Observation (P&O) technique. The LGWGCA is refined with a positional update mechanism inspired by the Grey Wolf Optimization (GWO) algorithm, optimizing the distribution of solution agents, while a Levy flight strategy is employed to reduce excessive randomness during agent replacement and recombination, thereby accelerating the tracking process. As the algorithm approaches the maximum power point, it seamlessly transitions to the P&O method, leveraging its rapid convergence to ensure precise and efficient power point identification. Experimental results demonstrate that the LGWGCA-P&O method achieves a PV conversion efficiency exceeding 99.96%, significantly minimizing power losses during MPPT. Furthermore, the method enhances convergence speed by approximately 40% compared to traditional GWO-P&O approaches, and it consistently outperforms Particle Swarm Optimization and Cuckoo Search Optimization algorithms under extreme conditions, demonstrating superior computational efficiency and robustness.

Impact localization of CFRP laminate based on FBG sensing and GWO-BP neural network

Carbon fiber reinforced plastic (CFRP) structures are susceptible to external energy impact during service, resulting in invisible damage. Therefore, accurate localization of these impact sites is crucial for the maintenance and safety of CFRP structures. In this paper, the strain data of the impact response of the CFRP laminate is obtained by using the fiber grating (FBG) sensor, and the data are mapped to the impact location by BP neural network. However, the traditional BP neural network is susceptible to local minima and slow convergence speed. Therefore, Grey Wolf optimization algorithm (GWO) is introduced in this study to optimize the BP neural network, which improves the convergence speed and positioning accuracy of the model. The experimental results show that the GWO-BP neural network model has excellent performance in impact positioning, with the maximum positioning error of 17.01 mm and the average positioning error of 13.05 mm. Compared with other machine learning methods, it has higher accuracy and efficiency.

Multi-Objective Optimization Algorithm Based Bidirectional Long Short Term Memory Network Model for Optimum Sizing of Distributed Generators and Shunt Capacitors for Distribution Systems

In this paper, a multi-objective grey wolf optimization (GWO) algorithm based Bidirectional Long Short Term Memory (BiLSTM) network machine learning (ML) model is proposed for finding the optimum sizing of distributed generators (DGs) and shunt capacitors (SHCs) to enhance the performance of distribution systems at any desired load factor. The stochastic traits of evolutionary computing methods necessitate running the algorithm repeatedly to confirm the global optimum. In order to save utility engineers time and effort, this study introduces a BiLSTM network-based machine learning model to directly estimate the optimal values of DGs and SHCs, rather than relying on load flow estimates. At first, a multi-objective grey wolf optimizer determines the most suitable locations and capacities of DGs and SHCs at the unity load factor and the same locations are used to obtain optimum sizing of DGs and SHCs at other load factors also. The base case data sets consisting of substation apparent power, real power load, reactive power load, real power loss, reactive power loss and minimum node voltage at various load factors in per unit values are taken as input training data for the machine learning model. The optimal sizes of the DGs and SHCs for the corresponding load factors obtained using GWO algorithm are taken as target data sets in per unit values for the machine learning model. An adaptive moment estimation (adam) optimization approach is employed to train the BiLSTM ML model for identifying the ideal values of distributed generations and shunt capacitors at different load factors. The efficacy of the proposed ML-based sizing algorithm is demonstrated via simulation studies.

A novel bearing weak fault diagnosis method based on rank constrained low-rank and sparse decomposition

Abstract Addressing the challenge of diagnosing incipient bearing faults amidst significant noise, a novel diagnostic approach is introduced, leveraging a Rank Constrained Low-Rank and Sparse Decomposition (RCLRSD) model tailored for weak fault detection in bearings. Initially, we raised the Autocorrelation Function of the Square Envelope in Frequency Domain (AFSEFD) as an innovative method for the estimation of fault frequencies. Subsequently, we constructed a two-dimensional observation matrix, which is formulated independently of predefined assumptions. Then, we examine the configuration and distribution patterns of bearing fault signals, uncovering the low-rank nature of fault characteristics within a designated two-dimensional transform domain. Moreover, we found the noise signal to exhibit sparsity and an approximate Gaussian distribution. Based on this, a rank-constrained low-rank sparse decomposition model is established, and rank-constrained low-rank regular constraints for feature information and sparse regular constraints and Gaussian constraints for interference signals are constructed respectively. Ultimately, we employed the Gray Wolf Optimization (GWO) algorithm to refine the model parameters, and we deduced the model's solver via the Alternating Direction Method of Multipliers (ADMM). The proposed RCLRSD model decomposes bearing fault data into three constituents: low-rank, sparse, and Gaussian components, effectively addressing the challenge of extracting weak fault signatures from bearings. The weak feature extraction capability of the RCLRSD model is verified using a multi-interference simulation model and experimental data of bearing failures under strong noise conditions; the generalization of the model is verified by the classification effect of the Support Vector Machine (SVM) under two bearing failure datasets. Comparison with various algorithms confirms the superiority of the proposed algorithm.

Inverse modeling of electromagnetic emissions using grey wolf optimization

The paper proposes an inverse modeling approach for evaluating radiated electromagnetic compatibility (EMC) in electronic components and circuits. To imitate the electromagnetic behavior of real devices, the suggested methodology generates an equivalent model using elementary dipoles. The Grey Wolf Optimization (GWO) algorithm solves an inverse problem to identify dipole parameters such as locations, intensities, and orientations. This technique improves model accuracy and robustness through a two-phase exploration and exploitation process that allows for effective parameter tuning while reducing simulation time and computational resources. Recent research in near-field measurement techniques illustrates the importance of reliable predictive methodologies in EMC evaluation. This method provides significant information on the impacts of power systems on surrounding electronic circuits, making it a useful tool for EMC testing. It also promotes the development of immunity models for high-frequency applications. Overall, this methodology simplifies the examination procedure, resulting in higher design efficiency for a wide range of electronic systems.

Techno-Economic Feasibility: Planning an On-Grid Solar Power System for Shrimp Pond Aeration

The rapid growth of the Indonesian shrimp farming industry is accompanied by high production costs, primarily driven by the reliance on fossil fuel-based energy sources that can destabilize the ecosystem. This study investigates the technical, economic, and environmental feasibility of using three energy sources, including photovoltaics (PV), grid, and generator, to supply aeration needs in shrimp ponds. A comparative analysis of three scenarios with on-grid schemes was conducted through optimization using Queen Honey Bee Migration (QHBM) and Grey Wolf Optimization (GWO) algorithms, namely Net Present Cost (NPC), Renewable Fraction (RF), and Carbon Emission (ECO2). From a technical point of view, a lower electricity tariff is obtained compared to the grid, which is US$ 589,968. The optimization results on the NPC, RF, and ECO2 parameters show that scenario 1 of the QHBM algorithm is the most optimal. This condition is evidenced by the acquisition of 3 parameters that are closest to the determination of the objective function, yielding an NPC of US$ 230,390.34, RF of 26.01%, and ECO2 of 1,484KgCO2e, with 655Wp PV specifications and the number of PV as many as 578pcs. Economically, the investment in a solar power plant for the shrimp pond obtained BEP of 4.2 years with a payback period (PP) obtained in year 5, net cash-flow of US$ 63,317.31, with ROI of 19% and NPV of US$ 775,159.40 in the same year.

Prediction of liquid bridge rupture between two plates combining artificial neural network with grey wolf optimization algorithm

Prediction of liquid bridge rupture between two plates combining artificial neural network with grey wolf optimization algorithm

A medical disease assisted diagnosis method based on lightweight fuzzy SZGWO-ELM neural network model.

The application of neural network model in intelligent diagnosis usually encounters challenges such as continuous adjustment of network parameters and significant cost in training the network facing numerous complex physiological data. To address this challenge, this paper introduces a fuzzy SZGWO-ELM neural network model for medical disease aid diagnosis with fuzzy membership function and ELM network to refine the improved Gray Wolf optimization algorithm. Firstly, the Z-type membership function is introduced as the inertia weight to get a balance for the grey wolf in seeking the optimal solution globally and locally and ensuring fast convergence. Secondly, the S-type membership function is utilized as the adaptive weight to flexibly adjust the grey wolf search step size to facilitate a quick approximation of the optimal solution. Finally, the improved Gray Wolf optimization algorithm is used to optimize the parameters of the ELM neural network model, termed as SZGWO-ELM. This method can eliminate the need for extensive network parameter adjustments and quickly locate the optimal solution to the problem using a lightweight neural network. The performance of the SZGWO is assessed by using metrics like convergence, mean, and standard deviation. Multiple experiments reveal that this method shows superior performance. Furthermore, five publicly accessible medical disease datasets from UCI were conducted to evaluate the performance of SZGWO-ELM network model comparing with different classify model, and the results in terms of precision, sensitivity, specificity and accuracy can achieve 99.52%, 94.14%, 99.26% and 96.08%, respectively, which illustrate that the proposed SZGWO-ELM neural network significantly enhance the model's accuracy, providing better support for doctors in disease diagnosis.

Multi-objective time-cost-safety risk trade-off optimization for the construction scheduling problem

PurposeThe purpose of this study is to enable the planning of construction projects with simultaneous consideration of time, cost and safety risks. It also aims to improve the decision-making process by evaluating the effectiveness of the Rao-2 algorithm in solving multi-objective time-cost-safety risk problems. In the end, this model is designed to support project managers in enhancing management approaches by addressing project challenges and constraints more efficiently.Design/methodology/approachIn this study, the Rao-2 algorithm, along with Grey Wolf Optimization (GWO) and Whale Optimization algorithm (WOA), were improved using the crowding distance-based non-dominated sorting method. Rao-2 was first compared to GWO and WOA. Subsequently, it was compared with well-established algorithms in the literature, including genetic algorithm (GA), particle swarm optimization (PSO) and differential evolution (DE). The C-metric, hypervolume and spread metrics were employed for performance measurement. The performance of the algorithms was evaluated on four case studies consisting of 11, 13, 18 and 25 activities.FindingsThe results revealed that Rao-2 performs better than other algorithms as the number of activities increases, when compared using the Hypervolume, Spread and C-metric measures. In terms of performance measures, the GWO algorithm outperformed Rao-2 in some evaluation metrics for the instance involving 11 activities. However, as the number of activities grew, the Rao-2 method consistently generated higher-quality Pareto fronts and outperformed GWO and WOA in all evaluation metrics. The solutions generated by Rao-2 were also superior to those obtained from GA, PSO and DE in all case studies, further demonstrating the capability of our framework to produce a wide range of optimal solutions with high diversity across different case studies.Originality/valueThis research demonstrates that Rao-2 not only improves solution quality when generating Pareto fronts but also achieves better results with fewer function evaluations compared to GA, PSO and DE. The algorithm's efficiency makes it particularly well-suited for optimizing time, cost and safety risks in large-scale construction projects, which in turn positions Rao-2 as a better choice for such projects by producing superior results compared to other algorithms. By providing high-quality solutions with reduced computational demands, Rao-2 offers a faster and more resource-efficient tool for decision-making, contributing to advancements in both the theory and practice of construction project management.

TDOA-AOA Localization Algorithm for 5G Intelligent Reflecting Surfaces

5G positioning technology has become deeply integrated into daily life. However, in wireless signal propagation environments, there may exist non-line-of-sight (NLOS) conditions, which lead to signal blockage and subsequently hinder the provision of positioning services. To address this issue, this paper proposes an intelligent reflecting surface (IRS) NLOS time difference of arrival–angle of arrival (TDOA-AOA) localization (INTAL) algorithm. First, the algorithm constructs a system model for 5G IRS localization, effectively overcoming the challenges of positioning in NLOS paths. Then, by applying the multiple signal classification algorithm to estimate the time delay and angle, and using the Chan algorithm to obtain the user’s estimated coordinates, an optimization problem is formulated to minimize the distance between the estimated and actual coordinates. The tent–snake optimization algorithm is employed to solve this optimization problem, thereby reducing localization errors. Finally, simulations demonstrate that the INTAL algorithm outperforms the snake optimization (SO) algorithm and the gray wolf optimization (GWO) algorithm under the same conditions, reducing the localization error by 56% and 60% on average, respectively. Additionally, when the signal-to-noise ratio is 30 dB, the localization error of the INTAL algorithm is only 0.2968 m, while the errors for the SO and GWO algorithms are 0.6733 m and 0.7398 m, respectively. This further proves the significant improvement of the algorithm in terms of localization accuracy.

Multi-Strategy Grey Wolf Optimization Algorithm for Global Optimization and Engineering Applications

Multi-Strategy Grey Wolf Optimization Algorithm for Global Optimization and Engineering Applications

Application of the Gray Wolf Optimization Algorithm and Neural Networks for Solving Discrete Problems

Relevance. In recent decades, metaheuristic optimization methods have become popular for solving complex problems that require searching for global extrema. Algorithms such as genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), as well as more modern approaches such as cat pack optimization (CSO) and gray wolf pack optimization (GWO) demonstrate high efficiency, but their application is often limited by the conditions of continuity and differentiability of the objective functions. This is a challenge when solving problems with discrete data, where such requirements are not met. In this context, the search for methods that allow adapting metaheuristic algorithms to work with discrete functions is of particular relevance.Aim. The study is aimed at testing the hypothesis about the possibility of using a neural network trained on a limited set of discrete data as an approximation of a function sufficient for the correct execution of the GWO algorithm when searching for a global minimum. The implementation of this hypothesis can significantly expand the scope of GWO, making it available for a wider range of problems where functions are defined on discrete sets.Methods. The study is based on the analysis of existing approaches and experimental verification of the hypothesis on two test functions: a linear function and a Booth function, which are widely used as standards for evaluating the performance of optimization algorithms. Numerical experiments were conducted using neural networks as an approximating model to obtain the results. Solution. During the experiments, an analysis of the applicability of neural networks for approximating discrete functions was carried out, which showed the success of this approach. It was found that neural networks can approximate discrete functions with high accuracy, creating conditions for a successful search for a global minimum using the GWO algorithm.Novelty. For the first time, a hypothesis was proposed and tested on the use of neural networks for approximating objective functions in metaheuristic optimization problems on discrete data. This direction has not previously received due coverage in the scientific literature, which adds significance to the obtained results and confirms the effectiveness of the proposed approach.Practical significance. The results of the study open up new prospects for the application of algorithms such as GWO in optimization problems based on discrete data, expanding the capabilities of metaheuristic methods and facilitating their implementation in a wider class of applied problems, including problems where the use of other methods is limited.

Enhancing accuracy in X-ray radiation-based multiphase flow meters: Integration of grey wolf optimization and MLP neural networks

This research investigates the development of an advanced predictive model aimed at accurately determining the volumetric percentages of water, oil, and gas within oil pipeline systems. Utilizing an innovative approach that incorporates an X-ray source alongside two sodium iodide detectors, the study leverages the Monte Carlo N-Particle (MCNP) simulation code to model the behavior of three-phase fluids under varied conditions. The model meticulously simulates various volumetric configurations of water, oil, and gas, resulting in a comprehensive dataset that provides key spectral information. The initial phase involved the extraction of ten temporal and frequency-related features from each detector, culminating in a pool of twenty features. The analytical process then applied the Grey Wolf Optimization (GWO) algorithm to select the most indicative features for predictive modeling. Out of the initial set, seven features—short-time energy, frequency deviation, relative spectral density, spectral margin, main peak position, spectral coefficient, and frequency intensity—were identified as critical for enhancing model accuracy. These features were subsequently fed into a meticulously structured multilayer perceptron (MLP) neural network. This network, designed with two hidden layers containing 20 and 10 neurons, respectively, demonstrated exceptional capability, achieving a root mean square error (RMSE) of less than 0.06 in the prediction of oil and gas volumetric percentages. The study emphasizes the significant impact of integrating refined feature selection techniques and robust neural network architectures on the precision and reliability of volumetric predictions in multiphase flow systems within oil pipelines. This approach not only enhances predictive accuracy but also contributes to more efficient resource management and operational planning in the oil and gas industry.

Convergence of Hybrid Grey Wolf Optimization with Heuristic Approaches for Enhanced Job Shop Scheduling

This scholarly inquiry examines the utilization of the Hybrid Grey Wolf Optimization Algorithm (HGWOA) in addressing the Job Shop Scheduling Problem (JSSP), a combinatorial optimization problem commonly encountered within production management. The central aim is to minimize makespan, defined as the cumulative duration necessary to finalize all tasks on a designated set of machines while observing precedence constraints. Conventional Optimization methodologies frequently encounter difficulties with intricate instances of JSSP owing to its NP-hard classification. We introduce a ground-breaking method the Grey Wolf Optimization Algorithm (GWOA) with various meta-heuristic strategies to augment its fruitfulness in resolving JSSP. The multiple findings underscore the usefulness of HGWOA, highlighting its prospective applicability in real-world contexts of production scheduling and management.

GWO-ANFIS-RD3PG: A reinforcement learning approach with dynamic adjustment and dual replay mechanism for building energy forecasting

Reliable prediction of building energy consumption is essential for effective and sustainable energy management. Traditional forecasting methods, however, face challenges when dealing with sudden changes in energy consumption. To address the above-mentioned issue, we introduce the GWO-ANFIS-RD3PG prediction model in this paper. This model aims to ensure global forecasting accuracy while minimizing prediction errors at sudden change points. A Grey Wolf Optimization (GWO) algorithm with the Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed to predict the energy consumption type and its fluctuation magnitude for the next time step. Within the Recurrent Dual Experience Replay Buffer DDPG (RD3PG) prediction module, we employ Long Short-Term Memory (LSTM), as the actor network in the Deep Deterministic Policy Gradient (DDPG), to account for long-term dependencies in time series data. Notably, we introduce an adaptive action modification mechanism that allows the agent to optimize actions based on the output of the aforementioned ANFIS, enabling real-time adjustments during sudden change. To further enhance the model’s performance, we adopt a dual experience replay mechanism to store data samples from sudden change points, capturing insight information during these anomalies. Experimental results on two real-world datasets demonstrate that this method reduces MAE by 5.81% and 13.47%, and RMSE by 5.69% and 15.21% compared to the state-of-art models, showing the significance of the proposed method for energy efficiency improvement in real-world building operations.