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Journal articles on the topic 'Gray wolf optimizer'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Medjahed, S. A., T. Ait Saadi, A. Benyettou, and M. Ouali. "Gray Wolf Optimizer for hyperspectral band selection." Applied Soft Computing 40 (March 2016): 178–86. http://dx.doi.org/10.1016/j.asoc.2015.09.045.

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Jiang, Hao, Jiuxiang Song, Baowei Zhang, and Yonghua Wang. "Yarn unevenness prediction using generalized regression neural network under various optimization algorithms." Journal of Engineered Fibers and Fabrics 17 (January 2022): 155892502210930. http://dx.doi.org/10.1177/15589250221093019.

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Unevenness is one of the important parameters for evaluating yarn quality, but the current prediction accuracy of yarn unevenness is low. One of the important reasons is that there are few sample dataset for yarn unevenness prediction. For this problem, this paper applies generalized regression neural network to predict the unevenness of the yarn. Then, the generalized regression neural network is optimized by using particle swarm optimization, fruit fly optimization algorithm, and gray wolf optimizer, respectively. Finally, the optimized models were experimentally validated for their effectiv

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ALJRIBI, Salem Faraj, and Ziyodulla YUSUPOV. "Gray Wolf, Mikro Şebekede Pil Depolama Dahil Optimize Edilmiş Ekonomik Yük Dağıtımı." Journal of Polytechnic 27, no. 1 (2021): 27–33. http://dx.doi.org/10.2339/politeknik.886712.

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In last decades, grey wolf optimizer algorithm as a new meta-heuristic optimization technique plays major role in optimization of engineering problems such as load forecasting, controller parameter tuning and job scheduling. In this paper, grey wolf optimization (GWO) is used to optimize the microgrid system for effective dispatching of power to load with economic manner. The model of microgrid system components are developed and investigated in the MATLAB/Simulink platform. The vital objective of the proposed grey wolf algorithm is to minimize overall cost of the microgrid operation. The deta

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Kumar, Anuj, Sangeeta Pant, and Mangey Ram. "System Reliability Optimization Using Gray Wolf Optimizer Algorithm." Quality and Reliability Engineering International 33, no. 7 (2016): 1327–35. http://dx.doi.org/10.1002/qre.2107.

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Puteri Baharie, Sri Rossa Aisyah, Sugiyarto Surono, and Aris Thobirin. "Hybrid Gradient Descent Grey Wolf Optimizer for Machine Learning Performance Enhancement." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 9, no. 1 (2025): 146–52. https://doi.org/10.29207/resti.v9i1.6203.

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Advancements in machine learning have enabled the development of more accurate and efficient health prediction models. This study aims to improve diabetes prediction performance using the Support Vector Machine (SVM) model optimized with the Hybrid Gradient Descent Gray Wolf Optimizer (HGD-GWO) method. SVM is a robust machine learning algorithm for classification and regression. Still, its performance depends significantly on selecting appropriate hyperparameters such as regularization (C), kernel coefficient (γ), and polynomial kernel degree (d). The HGD-GWO method synergizes gradient descent

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Kumar, Vikas, Kanak Kalita, S. Madhu, Uvaraja Ragavendran, and Xiao-Zhi Gao. "A Hybrid Genetic Programming–Gray Wolf Optimizer Approach for Process Optimization of Biodiesel Production." Processes 9, no. 3 (2021): 442. http://dx.doi.org/10.3390/pr9030442.

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Biodiesel is one the most sought after alternate fuels in the current global need for sustainable and renewable energy sources due to their lower emissions and no major modification requirement to existing engines. However, the performance and productivity of the biodiesel production process are significantly dependent on the process parameters. In this regard, a novel hybrid genetic programming-gray wolf optimizer approach for the process optimization of biodiesel production is proposed in this paper. For an illustration of the proposed approach, kinematic viscosity is expressed as a symbolic

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Knypiński, Łukasz. "Constrained optimization of line-start PM motor based on the gray wolf optimizer." Eksploatacja i Niezawodnosc - Maintenance and Reliability 23, no. 1 (2021): 1–10. http://dx.doi.org/10.17531/ein.2021.1.1.

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This paper presents the algorithm and computer software for constrained optimization based on the gray wolf algorithm. The gray wolf algorithm was combined with the external penalty function approach. The optimization procedure was developed using Borland Delphi 7.0. The developed procedure was then applied to design of a line-start PM synchronous motor. The motor was described by three design variables which determine the rotor structure. The multiplicative compromise function consisted of three maintenance parameters of designed motor and one non-linear constraint function was proposed. Next

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Ahmed, Sapan, and Dleen Salih. "IHS Image Fusion Based on Gray Wolf Optimizer (GWO)." Anbar Journal for Engineering Sciences 13, no. 1 (2022): 65–75. http://dx.doi.org/10.37649/aengs.2022.175882.

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Jia, Zhengzhao, Ziling Song, Junfu Fan, and Juyu Jiang. "Prediction of Blasting Fragmentation Based on GWO-ELM." Shock and Vibration 2022 (January 30, 2022): 1–8. http://dx.doi.org/10.1155/2022/7385456.

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Aiming at the complex nonlinear relationship among factors affecting blasting fragmentation, the input weight and hidden layer threshold of ELM (extreme learning machine) were optimized by gray wolf optimizer (GWO) and the prediction model of GWO-ELM blasting fragmentation was established. Taking No. 2 open-pit coal mine of Dananhu as an example, seven factors including the rock tensile strength, compressive strength, hole spacing, row spacing, minimum resistance line, super depth, and specific charge are selected as the input factors of the prediction model. The average size of blasting fragm

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Masoumi, Fariborz, Sina Masoumzadeh, Negin Zafari, and Mohammad Javad Emami-Skardi. "Optimum Sanitary Sewer Network Design Using Shuffled Gray Wolf Optimizer." Journal of Pipeline Systems Engineering and Practice 12, no. 4 (2021): 04021055. http://dx.doi.org/10.1061/(asce)ps.1949-1204.0000597.

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Xue, Haoran, Shouyin Lu, and Chengbin Zhang. "An Adaptive Control Based on Improved Gray Wolf Algorithm for Mobile Robots." Applied Sciences 14, no. 16 (2024): 7092. http://dx.doi.org/10.3390/app14167092.

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In this paper, a novel intelligent controller for the trajectory tracking control of a nonholonomic mobile robot with time-varying parameter uncertainty and external disturbances in the case of tire hysteresis loss is proposed. Based on tire dynamics principles, a dynamic and kinematic model of a nonholonomic mobile robot is established, and the neural network approximation model of the system’s nonlinear term caused by many coupling factors when the robot enters a roll is given. Then, in order to adaptively estimate the unknown upper bounds on the uncertainties and perturbations for each subs

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Shu, Yongkang, Zhenzhong Shen, Liqun Xu, Junrong Duan, Luyi Ju, and Qi Liu. "Inverse Modeling of Seepage Parameters Based on an Improved Gray Wolf Optimizer." Applied Sciences 12, no. 17 (2022): 8519. http://dx.doi.org/10.3390/app12178519.

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The seepage parameters of the dam body and dam foundation are difficult to determine accurately and quickly. Based on the inverse analysis, a Gray Wolf Optimizer (GWO) was introduced into this study to search the target hydraulic conductivity. A novel approach for initialization, a polynomial-based nonlinear convergence factor, and weighting factors based on Euclidean norms and hierarchy were applied to improve GWO. The practicability and effectiveness of Improved Gray Wolf Optimizer (IGWO) were evaluated by numerical experiments. Taking Kakiwa dam located on the Muli River of China as a case,

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Tian, Hailin, and Fang Wang. "Application of Gray Wolf Optimization Algorithm in Urban Electricity Load Forecasting Model." Journal of Physics: Conference Series 2592, no. 1 (2023): 012080. http://dx.doi.org/10.1088/1742-6596/2592/1/012080.

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Abstract A combined prediction model based on long short-term memory neural network (LSTM) and convolutional neural network (CNN) is proposed in order to increase the prediction accuracy of short-term load. To address the issue that the gray wolf optimization (GWO) search process is prone to falling into local optimum. An improved grey wolf algorithm (IGWO) is proposed to update the convergence factor using the lower incomplete gamma function to improve the global optimization performance. The Dropout technique is used to improve the generalization ability of the model; the network layers are

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Igiri, Chinwe P., Yudhveer Singh, and Ramesh C. Poonia. "A Review Study of Modified Swarm Intelligence: Particle Swarm Optimization, Firefly, Bat and Gray Wolf Optimizer Algorithms." Recent Advances in Computer Science and Communications 13, no. 1 (2020): 5–12. http://dx.doi.org/10.2174/2213275912666190101120202.

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Background: Limitations exist in traditional optimization algorithms. Studies show that bio-inspired alternatives have overcome these drawbacks. Bio-inspired algorithm mimics the characteristics of natural occurrences to solve complex problems. Particle swarm optimization, firefly algorithm, bat algorithms, gray wolf optimizer, among others are examples of bio-inspired algorithms. Researchers make certain assumptions while designing these models which limits their performance in some optimization domains. Efforts to find a solution to deal with these challenges leads to the multiplicity of var

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Khan, Muhammad Fahad, Muqaddas Bibi, Farhan Aadil, and Jong-Weon Lee. "Adaptive Node Clustering for Underwater Sensor Networks." Sensors 21, no. 13 (2021): 4514. http://dx.doi.org/10.3390/s21134514.

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Monitoring of an underwater environment and communication is essential for many applications, such as sea habitat monitoring, offshore investigation and mineral exploration, but due to underwater current, low bandwidth, high water pressure, propagation delay and error probability, underwater communication is challenging. In this paper, we proposed a sensor node clustering technique for UWSNs named as adaptive node clustering technique (ANC-UWSNs). It uses a dragonfly optimization (DFO) algorithm for selecting ideal measure of clusters needed for routing. The DFO algorithm is inspired by the sw

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Li, Shijie, Tong Wu, Kai Zhong, et al. "Gluing Atmospheric Lidar Signals Based on an Improved Gray Wolf Optimizer." Remote Sensing 15, no. 15 (2023): 3812. http://dx.doi.org/10.3390/rs15153812.

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Lidar is important active remote sensing equipment in the field of atmospheric environment detection. However, the detection range of lidar is severely limited by the dynamic range of photodetectors. To solve this problem, atmospheric lidars are often equipped with two or more channels to receive signals from different altitude ranges, where gluing the multi-channel echo signals becomes a key issue for accurate data inversion. In this paper, a multi-channel signal gluing algorithm based on the Improved Gray Wolf Optimizer (IGWO) and Neighborhood Rough Set (NRS), named IGWO-RSD, is proposed. Th

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Zhang, Yijie, and Yuhang Cai. "Explorative Binary Gray Wolf Optimizer with Quadratic Interpolation for Feature Selection." Biomimetics 9, no. 10 (2024): 648. http://dx.doi.org/10.3390/biomimetics9100648.

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The high dimensionality of large datasets can severely impact the data mining process. Therefore, feature selection becomes an essential preprocessing stage, aimed at reducing the dimensionality of the dataset by selecting the most informative features while improving classification accuracy. This paper proposes a novel binary Gray Wolf Optimization algorithm to address the feature selection problem in classification tasks. Firstly, the historical optimal position of the search agent helps explore more promising areas. Therefore, by linearly combining the best positions of the search agents, t

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Jasim, Sarah S., Alia K. Abdul Hassan, and Scott Turner. "Driver Drowsiness Detection Using Gray Wolf Optimizer Based on Voice Recognition." ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 10, no. 2 (2022): 142–51. http://dx.doi.org/10.14500/aro.11000.

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Globally, drowsiness detection prevents accidents. Blood biochemicals, brain impulses, etc., can measure tiredness. However, due to user discomfort, these approaches are challenging to implement. This article describes a voice-based drowsiness detection system and shows how to detect driver fatigue before it hampers driving. A neural network and Gray Wolf Optimizer are used to classify sleepiness automatically. The recommended approach is evaluated in alert and sleep-deprived states on the driver tiredness detection voice real dataset. The approach used in speech recognition is mel-frequency c

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Hu, Pei, Yibo Han, and Zheng Zhang. "Multi-Level Thresholding Color Image Segmentation Using Modified Gray Wolf Optimizer." Biomimetics 9, no. 11 (2024): 700. http://dx.doi.org/10.3390/biomimetics9110700.

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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

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Zhu, Yuchang, Li Ke, Yijing Wei, and Xiao Zheng. "A High-Precision Real-Time Temperature Acquisition Method Based on Magnetic Nanoparticles." Sensors 24, no. 23 (2024): 7716. https://doi.org/10.3390/s24237716.

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The unique magnetothermal properties of magnetic nanoparticles enable the development of a high-precision, real-time, noninvasive temperature measurement method with significant potential in the biomedical field. Based on a low-frequency alternating magnetic field excitation model, we construct two additional magnetic field excitation models—alternating current–direct current superposition and dual-frequency superposition—to extract harmonic amplitude components from the magnetization response. To increase the accuracy of harmonic information acquisition, the effects of the truncation error, e

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Mousavi-Kiasari, Seyyed Mohammad Ghasem, Kamyar Rashidi, Davood Fathi, Hussein Taleb, Seyed Mohammad Mirjalili, and Vahid Faramarzi. "Computational Design of Highly-Sensitive Graphene-Based Multilayer SPR Biosensor." Photonics 9, no. 10 (2022): 688. http://dx.doi.org/10.3390/photonics9100688.

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In this paper, we present a set of optimal graphene-based multilayer surface plasmon resonance (SPR) biosensors for highly sensitive detection of biomolecules. To optimize the biosensor structure, we employed a multi-objective gray wolf optimizer (MOGWO) to maximize the sensitivity and minimize the structure full width at half maximum (FWHM). The main advantages of the optimized structures are high sensitivity, low FWHM, as well as easy implementation. We developed an algorithm that enables us to achieve nine different optimized structures. The best sensitivity, FWHM and FOM are obtained equal

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Wang, Fenqin, and Zhaosu Liu. "Power Load Prediction Based on IGWO-BILSTM Network." Mathematical Problems in Engineering 2023 (April 14, 2023): 1–11. http://dx.doi.org/10.1155/2023/8996138.

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Recently, neural networks based on intelligent algorithms have been widely used in short-term power load prediction. However, these algorithms have poor reproducibility in the case of repetition. Aiming at the shortcomings of the gray wolf optimizer (GWO) algorithm, such as slow convergence speed and easy to fall into local optimum, an improved gray wolf optimizer (IGWO) was proposed. In order to better extract features from time series data, the improved grey wolf optimizer-bidirectional long short-term memory (IGWO-BILSTM) power load prediction model is established by bidirectional long shor

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Jasim, Sarah S., Alia K. Abdul Hassan, and Scott Turner. "Driver Drowsiness Detection Using Gray Wolf Optimizer Based on Face and Eye Tracking." ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 10, no. 1 (2022): 49–56. http://dx.doi.org/10.14500/aro.10928.

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It is critical today to provide safe and collision-free transport. As a result, identifying the driver’s drowsiness before their capacity to drive is jeopardized. An automated hybrid drowsiness classification method that incorporates the artificial neural network (ANN) and the gray wolf optimizer (GWO) is presented to discriminate human drowsiness and fatigue for this aim. The proposed method is evaluated in alert and sleep-deprived settings on the driver drowsiness detection of video dataset from the National Tsing Hua University Computer Vision Lab. The video was subjected to various video a

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Li, Su, Zhihong Yan, Jinxia Sha, et al. "Application of AMOGWO in Multi-Objective Optimal Allocation of Water Resources in Handan, China." Water 14, no. 1 (2021): 63. http://dx.doi.org/10.3390/w14010063.

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The reasonable allocation of water resources using different optimization technologies has received extensive attention. However, not all optimization algorithms are suitable for solving this problem because of its complexity. In this study, we applied an ameliorative multi-objective gray wolf optimizer (AMOGWO) to the problem. For AMOGWO, which is based on the multi-objective gray wolf optimizer, we improved the distance control parameter calculation method, added crowding degree for the archive, and optimized the selection mechanism for leader wolves. Subsequently, AMOGWO was used to solve t

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Zeng, Qingyao, Dapeng Xiong, Zhongwang Wu, Kechang Qian, Yu Wang, and Yinghao Su. "WolfFuzz: A Dynamic, Adaptive, and Directed Greybox Fuzzer." Electronics 13, no. 11 (2024): 2096. http://dx.doi.org/10.3390/electronics13112096.

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As the directed greybox fuzzing (DGF) technique advances, it is being extensively utilized in various fields such as defect reproduction, patch testing, and vulnerability identification. Nevertheless, current DGFs waste a significant amount of resources due to their simplistic distance definitions and overly straightforward energy distribution for the seeds. To address these issues, a dynamic distance-weighting-based distance estimation strategy is proposed first, which facilitates strategies for seed distribution that take energy into consideration. Second, to overcome the limitations of curr

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Osea, Zebua, Made Ginarsa I, and Made Ari Nrartha I. "GWO-based estimation of input-output parameters of thermal power plants." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 4 (2020): 2235–44. https://doi.org/10.12928/TELKOMNIKA.v18i4.12957.

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The fuel cost curve of thermal generators was very important in the calculation of economic dispatch and optimal power flow. Temperature and aging could make changes to fuel cost curve so curve estimation need to be done periodically. The accuracy of the curve parameters estimation strongly affected the calculation of the dispatch. This paper aims to estimate the fuel cost curve parameters by using the grey wolf optimizer method. The problem of curve parameter estimation was made as an optimization problem. The objective function to be minimized was the total number of absolute error or the di

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Abualhaj, Mosleh M., Qusai Y. Shambour, Ahmad Adel Abu-Shareha, Sumaya N. Al-Khatib, and Amal Amer. "Enhancing malware detection through self-union feature selection using gray wolf optimizer." Indonesian Journal of Electrical Engineering and Computer Science 37, no. 1 (2025): 197. http://dx.doi.org/10.11591/ijeecs.v37.i1.pp197-205.

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This research explores the impact of malware on the digital world and presents an innovative system to detect and classify malware instances. The suggested system combines a random forest (RF) classifier and gray wolf optimizer (GWO) to identify and detect malware effectively. Therefore, the suggested system is called RFGWO-Mal. The RFGWO-Mal system employs the GWO for feature selection in binary and multiclass classification scenarios. Then, the RFGWO-Mal system uses a novel self-union feature selection approach, combining features from different subsets of binary and multiclass classificatio

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Mosleh, M. Abualhaj Qusai Y. Shambour Ahmad Adel Abu-Shareha Sumaya N. Al-Khatib Amal Amer. "Enhancing malware detection through self-union feature selection using gray wolf optimizer." Indonesian Journal of Electrical Engineering and Computer Science 37, no. 1 (2025): 197–205. https://doi.org/10.11591/ijeecs.v37.i1.pp197-205.

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This research explores the impact of malware on the digital world and presents an innovative system to detect and classify malware instances. The suggested system combines a random forest (RF) classifier and gray wolf optimizer (GWO) to identify and detect malware effectively. Therefore, the suggested system is called RFGWO-Mal. The RFGWO-Mal system employs the GWO for feature selection in binary and multiclass classification scenarios. Then, the RFGWO-Mal system uses a novel self-union feature selection approach, combining features from different subsets of binary and multiclass classificatio

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Gokuldhev, M., G. Singaravel, and N. R. Ram Mohan. "Multi-Objective Local Pollination-Based Gray Wolf Optimizer for Task Scheduling Heterogeneous Cloud Environment." Journal of Circuits, Systems and Computers 29, no. 07 (2019): 2050100. http://dx.doi.org/10.1142/s0218126620501005.

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The rebel of global networked resource is Cloud computing and it shared the data to the users easily. With the widespread availability of network technologies, the user requests increase day by day. Nowadays, the foremost complication in Cloud technology is task scheduling. The cargo position and arrangement of the tasks are the two important parameters in the Cloud domain, which can provide the Quality of Service (QoS). In this paper, we formulated the optimal minimization of makespan and energy consumption in task scheduling using Local Pollination-based Gray Wolf Optimizer (LPGWO) algorithm

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Liu, Zhen, An-Ran Zhao, and Si-Lu Liu. "Prediction of Fading for Painted Cultural Relics Using the Optimized Gray Wolf Optimization-Long Short-Term Memory Model." Applied Sciences 14, no. 21 (2024): 9735. http://dx.doi.org/10.3390/app14219735.

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Cultural heritage digitization is of great significance for the protection, restoration, and rejuvenation of cultural relics. In particular, the investigation of fading mechanisms is essential for virtual restoration to accurately recreate the original appearance of artifacts and facilitate humanistic and historical research. For the purpose of investigating the color fading mechanism of pigments, we propose a color fading time series model using a combined long short-term memory recurrent neural network modified by the gray wolf optimization algorithm (GWOAD-LSTM). First, the general gray wol

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Adrian, Ronald, Anni Karimatul Fauziyyah, and Sahirul Alam. "Continuous Integration/ Continuous Delivery Optimization on Network Automation using Gray Wolf Optimizer." SISTEMASI 11, no. 3 (2022): 776. http://dx.doi.org/10.32520/stmsi.v11i3.2322.

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Continuous Integration/ Continuous Delivery is the latest method used in network automation. In-network programming has helped network admins a lot in managing all their devices. One of the real-time networks needs to force network admins to be able to provide data quickly. Deployment speed can be increased to provide up-to-date data or network configuration. To tackle these problems, we propose implementing the GWO algorithm in the Continuous Integration/Continuous Delivery process. This algorithm is proven to be superior in the speed of finding the value of the objective function compared to

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Sulaiman, Mohd Herwan, Zuriani Mustaffa, Mohd Rusllim Mohamed, and Omar Aliman. "Using the gray wolf optimizer for solving optimal reactive power dispatch problem." Applied Soft Computing 32 (July 2015): 286–92. http://dx.doi.org/10.1016/j.asoc.2015.03.041.

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Mirjalili, Seyed Mohammad, Hussein Taleb, M. Z. Kabir, and Pablo Bianucci. "Design optimization of orbital angular momentum fibers using the gray wolf optimizer." Applied Optics 59, no. 20 (2020): 6181. http://dx.doi.org/10.1364/ao.391731.

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Qi, Jihui, Yanchao Wang, Zhengfeng Bai, and Xuyun Fu. "Dynamic Adjustment of Long-Term Maintenance Plan Based on Gray Wolf Optimizer." E3S Web of Conferences 512 (2024): 01010. http://dx.doi.org/10.1051/e3sconf/202451201010.

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Aeroengine is an expensive large-scale precision and complex equipment, in the operation process needs to be repaired and maintained many times. The current management of civil aeroengine is generally aeroengine fleet as a unit, the development of maintenance plans, but once the maintenance plan is determined, if encountered unplanned disturbances, such as aeroengine repair ahead of schedule and other emergencies, the maintenance plan cannot make self-adjustment, it is difficult to be applied to the engineering practice. In view of the above problems, this paper proposes and establishes a dyna

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Li, Jun, and Kexue Sun. "Pressure Vessel Design Problem Using Improved Gray Wolf Optimizer Based on Cauchy Distribution." Applied Sciences 13, no. 22 (2023): 12290. http://dx.doi.org/10.3390/app132212290.

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The Gray Wolf Optimizer (GWO) is an established algorithm for addressing complex optimization tasks. Despite its effectiveness, enhancing its precision and circumventing premature convergence is crucial to extending its scope of application. In this context, our study presents the Cauchy Gray Wolf Optimizer (CGWO), a modified version of GWO that leverages Cauchy distributions for key algorithmic improvements. The innovation of CGWO lies in several areas: First, it adopts a Cauchy distribution-based strategy for initializing the population, thereby broadening the global search potential. Second

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Tuan, Dao Huy, Dao Trong Tran, Van Nguyen Ngoc Thanh, and Van Van Huynh. "Load Frequency Control Based on Gray Wolf Optimizer Algorithm for Modern Power Systems." Energies 18, no. 4 (2025): 815. https://doi.org/10.3390/en18040815.

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The increasing complexity of modern power systems (MPSs), driven by the integration of renewable energy sources and multi-area configurations, demands robust and adaptive load frequency control (LFC) strategies. This paper proposes a novel approach to the LFC of the MPS by integrating a proportional–integral–derivative (PID) controller optimized using the gray wolf optimizer (GWO) algorithm. The effectiveness of the GWO-PID method is evaluated on multi-area power systems, including systems integrated with wind energy. The GWO-PID controller shows superior frequency stability, achieving deviati

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Manakhari, Sushanth, and Yanzhen Qu. "Improving the Accuracy and Performance of Deep Learning Model by Applying Hybrid Grey Wolf Whale Optimizer to P&C Insurance Data." European Journal of Electrical Engineering and Computer Science 7, no. 4 (2023): 17–26. http://dx.doi.org/10.24018/ejece.2023.7.4.548.

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The insurance industry is based on risk calculations, high profits, and detailed information. The predictive models that insurance companies utilize allow insurance companies to make accurate decisions about the insurance sector. This research focuses on improving the accuracy of predicting customers of Property and Casualty (P&C) insurance. In this study, a reliable quantitative analytical big data method has been developed, and the Hybrid Grey Wolf and Whale Optimization (HGWWO) is utilized with Deep Learning Model for evaluating customer behavior of the customers of P&C insurance. T

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Yang, Geying, Lina Wang, Rongwei Yu, Junjiang He, Bo Zeng, and Tian Wu. "A Modified Gray Wolf Optimizer-Based Negative Selection Algorithm for Network Anomaly Detection." International Journal of Intelligent Systems 2023 (February 24, 2023): 1–23. http://dx.doi.org/10.1155/2023/8980876.

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Intrusion detection systems are crucial in fighting against various network attacks. By monitoring the network behavior in real time, possible attack attempts can be detected and acted upon. However, with the development of openness and flexibility of networks, artificial immunity-based network anomaly detection methods lack continuous adaptability and hence have poor detection performance. Thus, a novel framework for network anomaly detection with adaptive regulation is built in this paper. First, a heuristic dimensionality reduction algorithm based on unsupervised clustering is proposed. Thi

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Alghamdi, Ali S. "Greedy Sine-Cosine Non-Hierarchical Grey Wolf Optimizer for Solving Non-Convex Economic Load Dispatch Problems." Energies 15, no. 11 (2022): 3904. http://dx.doi.org/10.3390/en15113904.

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Economic load dispatch (ELD) provides significant benefits to the operation of the power system. It appears to be a complex nonconvex optimization problem subject to several equal and unequal constraints. The greedy sine-cosine nonhierarchical gray wolf optimizer (G-SCNHGWO) is introduced in this study to solve complex nonconvex ELD optimization problems efficiently and robustly. The sine and cosine functions assist the search agents of the grey wolf optimizer (GWO) algorithm in avoiding trapping in a local optimum. In addition, the greedy nonhierarchical concept is integrated into GWO to enri

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Agouzoul, Naima, Aziz Oukennou, Faissal Elmariami, Jamal Boukherouaa, and Rabiaa Gadal. "Power efficiency improvement in reactive power dispatch under load uncertainty." Power efficiency improvement in reactive power dispatch under load uncertainty 14, no. 4 (2024): 3616–27. https://doi.org/10.11591/ijece.v14i4.pp3616-3627.

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Nowadays, there is a significant rise in electricity demand, posing challenges for power grid operators due to inaccurate forecasting, leading to excessive power losses and voltage instability. This paper addresses these issues by focusing on solving optimal reactive power dispatch (ORPD) while considering load demand uncertainty. The main objective of solving ORPD is to reduce power losses by adjusting generator voltage ratings, transformer tap ratio, and shunt capacitors' reactive power. Monte Carlo simulation (MCS) is employed to generate load scenarios us

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Wu, Zhizhong. "Deep Learning with Improved Metaheuristic Optimization for Traffic Flow Prediction." Journal of Computer Science and Technology Studies 6, no. 4 (2024): 47–53. http://dx.doi.org/10.32996/jcsts.2024.6.4.7.

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Aiming at the current dilemma of inaccurate prediction accuracy in the field of traffic flow prediction, this paper proposes a novel traffic flow prediction method using the Revised Enhanced Extreme Gray Wolf Optimizer (REEGWO) to optimize convolutional neural networks (CNNs) and combining with bi-directional long and short-term memory (BiLSTM) networks. The experimental results show that the model can effectively converge the training loss error and RMSE, and significantly outperforms the existing classical methods in terms of goodness-of-fit, average absolute error, average deviation error a

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Sarah, Saadoon Jasim, and Karim Abdul Hassan Alia. "Driving sleepiness detection using electrooculogram analysis and grey wolf optimizer." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 6 (2022): 6034–44. https://doi.org/10.11591/ijece.v12i6.pp6034-6044.

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In modern society, providing safe and collision-free travel is essential. Therefore, detecting the drowsiness state of the driver before its ability to drive is compromised. For this purpose, an automated hybrid sleepiness classification system that combines the artificial neural network and gray wolf optimizer is proposed to distinguish human Sleepiness and fatigue. The proposed system is tested on data collected from 15 drivers (male and female) in alert and sleep-deprived conditions where physiological signals are used as sleep markers. To evaluate the performance of the proposed algorithm,

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Bi, Chunguang, Qiaoyun Tian, He Chen, et al. "Optimizing a Multi-Layer Perceptron Based on an Improved Gray Wolf Algorithm to Identify Plant Diseases." Mathematics 11, no. 15 (2023): 3312. http://dx.doi.org/10.3390/math11153312.

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Metaheuristic optimization algorithms play a crucial role in optimization problems. However, the traditional identification methods have the following problems: (1) difficulties in nonlinear data processing; (2) high error rates caused by local stagnation; and (3) low classification rates resulting from premature convergence. This paper proposed a variant based on the gray wolf optimization algorithm (GWO) with chaotic disturbance, candidate migration, and attacking mechanisms, naming it the enhanced gray wolf optimizer (EGWO), to solve the problem of premature convergence and local stagnation

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Jasim, Sarah Saadoon, and Alia Karim Abdul Hassan. "Driving sleepiness detection using electrooculogram analysis and grey wolf optimizer." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 6 (2022): 6034. http://dx.doi.org/10.11591/ijece.v12i6.pp6034-6044.

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<span lang="EN-US">In modern society, providing safe and collision-free travel is essential. Therefore, detecting the drowsiness state of the driver before its ability to drive is compromised. For this purpose, an automated hybrid sleepiness classification system that combines the artificial neural network and gray wolf optimizer is proposed to distinguish human Sleepiness and fatigue. The proposed system is tested on data collected from 15 drivers (male and female) in alert and sleep-deprived conditions where physiological signals are used as sleep markers. To evaluate the performance o

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Dehghani, Mohammad, Zeinab Montazeri, Ali Dehghani, et al. "MLO: Multi Leader Optimizer." International Journal of Intelligent Engineering and Systems 13, no. 6 (2020): 364–73. http://dx.doi.org/10.22266/ijies2020.1231.32.

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Optimization is a topic that has always been discussed in all different fields of science. One of the most effective techniques for solving such problems is optimization algorithms. In this paper, a new optimizer called Multi-Leader optimizer (MLO) is developed in which multiple leaders guide members of the population towards the optimal answer. MLO is mathematically modelled based on the process of advancing members of the population and following the leaders. MLO performance in optimization is examined on twenty-three standard objective functions. The results of this optimization are compare

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Hu, Cong, Yuanxiang Chen, Shangbin Sun, Jia Fu, and Jianguo Yu. "Optimization Method of Microwave Devices Based on Improved Extreme Learning Machine." Journal of Physics: Conference Series 2245, no. 1 (2022): 012005. http://dx.doi.org/10.1088/1742-6596/2245/1/012005.

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Abstract With the rapid development of modern wireless technology, it is necessary to continuously optimize microwave devices to meet the higher requirements of communication systems. In this paper, we propose a microwave device optimization method based on extreme learning machine (ELM) and gray wolf optimizer (GWO). we adopt GWO to optimize the parameters of ELM and establish the mapping relationships between the microwave device design parameters and their responses. According to the inverse mapping of expected response, the expected design parameters of microwave devices are obtained. The

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Zhou, Yichen, Xiaohui Yang, Lingyu Tao, and Li Yang. "Transformer Fault Diagnosis Model Based on Improved Gray Wolf Optimizer and Probabilistic Neural Network." Energies 14, no. 11 (2021): 3029. http://dx.doi.org/10.3390/en14113029.

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Dissolved gas analysis (DGA) based in insulating oil has become a more mature method in the field of transformer fault diagnosis. However, due to the complexity and diversity of fault types, the traditional modeling method based on oil sample analysis is struggling to meet the industrial demand for diagnostic accuracy. In order to solve this problem, this paper proposes a probabilistic neural network (PNN)-based fault diagnosis model for power transformers and optimizes the smoothing factor of the pattern layer of PNN by the improved gray wolf optimizer (IGWO) to improve the classification acc

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Liu, Jing, Zhentian Liu, Yang Wu, and Keqin Li. "MBB-MOGWO: Modified Boltzmann-Based Multi-Objective Grey Wolf Optimizer." Sensors 24, no. 5 (2024): 1502. http://dx.doi.org/10.3390/s24051502.

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The primary objective of multi-objective optimization techniques is to identify optimal solutions within the context of conflicting objective functions. While the multi-objective gray wolf optimization (MOGWO) algorithm has been widely adopted for its superior performance in solving multi-objective optimization problems, it tends to encounter challenges such as local optima and slow convergence in the later stages of optimization. To address these issues, we propose a Modified Boltzmann-Based MOGWO, referred to as MBB-MOGWO. The performance of the proposed algorithm is evaluated on multiple mu

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Shu, Xiaosong, Tengfei Bao, Yuhan Hu, Yangtao Li, and Kang Zhang. "Camera calibration method using synthetic speckle pattern with an improved gray wolf optimizer algorithm." Applied Optics 60, no. 34 (2021): 10477. http://dx.doi.org/10.1364/ao.444593.

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Wang, Hao, Bahman Arasteh, Keyvan Arasteh, Farhad Soleimanian Gharehchopogh, and Alireza Rouhi. "A software defect prediction method using binary gray wolf optimizer and machine learning algorithms." Computers and Electrical Engineering 118 (August 2024): 109336. http://dx.doi.org/10.1016/j.compeleceng.2024.109336.

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