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Journal articles on the topic 'GWO'

Author: Grafiati

Published: 16 January 2024

Last updated: 25 July 2025

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1

Liu, Guangwei, Zhiqing Guo, Wei Liu, Feng Jiang, and Ensan Fu. "A feature selection method based on the Golden Jackal-Grey Wolf Hybrid Optimization Algorithm." PLOS ONE 19, no. 1 (2024): e0295579. http://dx.doi.org/10.1371/journal.pone.0295579.

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This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). The primary objective of this method is to create an effective data dimensionality reduction technique for eliminating redundant, irrelevant, and noisy features within high-dimensional datasets. Drawing inspiration from the Chinese idiom “Chai Lang Hu Bao,” hybrid algorithm mechanisms, and cooperative behaviors observed in natural animal populations, we amalgamate the GWO algorithm, the Lagrange interpolation method, and the G

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2

Ikram, Misbah, Hongbo Liu, Ahmed Mohammed Sami Al-Janabi, et al. "Enhancing the Prediction of Influent Total Nitrogen in Wastewater Treatment Plant Using Adaptive Neuro-Fuzzy Inference System–Gradient-Based Optimization Algorithm." Water 16, no. 21 (2024): 3038. http://dx.doi.org/10.3390/w16213038.

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For the accurate estimation of daily influent total nitrogen of sewage plants, a novel hybrid approach is proposed in this study, where a gradient-based optimization (GBO) algorithm is employed to adjust the hyper-parameters of an adaptive neuro-fuzzy system (ANFIS). Several benchmark methods for optimizing ANFIS parameters are compared, which include particle swarm optimization (PSO), gray wolf optimization (GWO), and gradient-based optimization (GBO). The prediction accuracy of the ANFIS-GBO model is evaluated against other models using four statistical measures: root-mean-squared error (RMS

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Liu, Yuanyuan, Jiahui Sun, Haiye Yu, Yueyong Wang, and Xiaokang Zhou. "An Improved Grey Wolf Optimizer Based on Differential Evolution and OTSU Algorithm." Applied Sciences 10, no. 18 (2020): 6343. http://dx.doi.org/10.3390/app10186343.

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Aimed at solving the problems of poor stability and easily falling into the local optimal solution in the grey wolf optimizer (GWO) algorithm, an improved GWO algorithm based on the differential evolution (DE) algorithm and the OTSU algorithm is proposed (DE-OTSU-GWO). The multithreshold OTSU, Tsallis entropy, and DE algorithm are combined with the GWO algorithm. The multithreshold OTSU algorithm is used to calculate the fitness of the initial population. The population is updated using the GWO algorithm and the DE algorithm through the Tsallis entropy algorithm for crossover steps. Multithres

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Liu, Haiqiang, Gang Hua, Hongsheng Yin, and Yonggang Xu. "An Intelligent Grey Wolf Optimizer Algorithm for Distributed Compressed Sensing." Computational Intelligence and Neuroscience 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/1723191.

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Distributed Compressed Sensing (DCS) is an important research area of compressed sensing (CS). This paper aims at solving the Distributed Compressed Sensing (DCS) problem based on mixed support model. In solving this problem, the previous proposed greedy pursuit algorithms easily fall into suboptimal solutions. In this paper, an intelligent grey wolf optimizer (GWO) algorithm called DCS-GWO is proposed by combining GWO and q-thresholding algorithm. In DCS-GWO, the grey wolves’ positions are initialized by using the q-thresholding algorithm and updated by using the idea of GWO. Inheriting the g

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Widians, Joan Angelina, Retantyo Wardoyo, and Sri Hartati. "A Hybrid Ant Colony and Grey Wolf Optimization Algorithm for Exploitation-Exploration Balance." Emerging Science Journal 8, no. 4 (2024): 1642–54. http://dx.doi.org/10.28991/esj-2024-08-04-023.

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The Ant Colony Optimization (ACO) and Grey Wolf Optimizer (GWO) are well-known nature-inspired algorithms. ACO is a metaheuristic search algorithm that takes inspiration from the behavior of real ants. In contrast, GWO is a grey wolf population-based heuristic algorithm. The important procedure in optimization is exploration and exploitation. ACO has excellent global and local search capabilities, and the exploration process is performed better than the exploitation process. In the case of regular, GWO is a greatly competitive algorithm compared to other common meta-heuristic algorithms, as it

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Zheng, Yukun, Ruyue Sun, Yixiang Liu, Yanhong Wang, Rui Song, and Yibin Li. "A Hybridization Grey Wolf Optimizer to Identify Parameters of Helical Hydraulic Rotary Actuator." Actuators 12, no. 6 (2023): 220. http://dx.doi.org/10.3390/act12060220.

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Based on the grey wolf optimizer (GWO) and differential evolution (DE), a hybridization algorithm (H-GWO) is proposed to avoid the local optimum, improve the diversity of the population, and compromise the exploration and exploitation appropriately. The mutation and crossover principles of the DE algorithm are introduced into the GWO algorithm, and the opposition-based optimization learning technology is combined to update the GWO population to increase the population diversity. The algorithm is then benchmarked against nine typical test functions and compared with other state-of-the-art meta-

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Zhao, Jiankun, Wei Liu, Qingxian Zhang, et al. "Rapid localization of radioactive leaks based on hybrid adaptive grey wolf algorithm." Journal of Instrumentation 17, no. 08 (2022): P08034. http://dx.doi.org/10.1088/1748-0221/17/08/p08034.

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Abstract Radioactive source localization algorithms have been widely used in the detection of nuclear accident areas. But some shortcomings, such as complex algorithm structure, slow localization speed and poor accuracy, were obviously performed to affect mobile robot locating autonomously. In this paper, a potential alternative method was investigated to be a new usage of locating leaks, just via specifying the change of exposure rate. In this model, several key factors, such as gamma ray attenuation, scattering factor, travel angle guide, spatial discretization, etc., were taken into conside

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8

Sheng, Long, Sen Wu, and Zongyu Lv. "Modified Grey Wolf Optimizer and Application in Parameter Optimization of PI Controller." Applied Sciences 15, no. 8 (2025): 4530. https://doi.org/10.3390/app15084530.

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The Grey Wolf Optimizer (GWO) is a well-known metaheuristic algorithm that currently has an extremely wide range of applications. However, with the increasing demand for accuracy, its shortcomings of low exploratory and population diversity are increasingly exposed. A modified Grey Wolf Optimizer (M-GWO) is proposed to tackle these weaknesses of the GWO. The M-GWO introduces mutation operators and different location-update strategies, achieving a balance between exploration and development. The experiment validated the performance of the M-GWO using the CEC2017 benchmark function and compared

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9

Shi-fan, Qiao, Tan Jun-kun, Zhang Yong-gang, et al. "Settlement Prediction of Foundation Pit Excavation Based on the GWO-ELM Model considering Different States of Influence." Advances in Civil Engineering 2021 (January 27, 2021): 1–11. http://dx.doi.org/10.1155/2021/8896210.

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This paper proposes a novel grey wolf optimization-extreme learning machine model, namely, the GWO-ELM model, to train and predict the ground subsidence by combining the extreme learning machine with the grey wolf optimization algorithm. Taking an excavation project of a foundation pit of Kunming in China as an example, after analyzing the settlement monitoring data of cross sections JC55 and JC56, the representative monitoring sites JC55-2 and JC56-1 were selected as the training monitoring samples of the GWO-ELM model. And three kinds of GWO-ELM models such as considering the influence of ti

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Alsaeedi, Lec Ali Hakem, Suha Muhammed Hadi, Yarub Alazzawi, and Emad Badry Badry. "Eight-Figure Pattern for Enhancing the Searching Process of Grey Wolf Optimization (Eight-GWO)." Wasit Journal for Pure sciences 4, no. 2 (2025): 20–31. https://doi.org/10.31185/wjps.718.

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Evolutionary algorithms suffer significantly from a stack at the local optima. This paper proposes a new strategy that detects when the search gets stuck in a local optimum and then switches to a more dynamic approach to escape. The proposed model is based on simulating eight pattern movements and embedded with a Grey Wolf Optimizer algorithm (GWO). It is called the Eight-Figure Grey Wolf Optimizer (Eight-GWO). The proposed model combines two phases: regular search when searching progresses over time while the second phase, searching by eight patterns when the algorithm reaches stuck. The Eigh

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Kohli, Mehak, and Sankalap Arora. "Chaotic grey wolf optimization algorithm for constrained optimization problems." Journal of Computational Design and Engineering 5, no. 4 (2017): 458–72. http://dx.doi.org/10.1016/j.jcde.2017.02.005.

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Abstract The Grey Wolf Optimizer (GWO) algorithm is a novel meta-heuristic, inspired from the social hunting behavior of grey wolves. This paper introduces the chaos theory into the GWO algorithm with the aim of accelerating its global convergence speed. Firstly, detailed studies are carried out on thirteen standard constrained benchmark problems with ten different chaotic maps to find out the most efficient one. Then, the chaotic GWO is compared with the traditional GWO and some other popular meta-heuristics viz. Firefly Algorithm, Flower Pollination Algorithm and Particle Swarm Optimization

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Milenković, Branislav, Mladen Krstić, and Đorđe Jovanović. "Application of grey wolf algorithm for solving engineering optimization problems." Tehnika 76, no. 1 (2021): 50–57. http://dx.doi.org/10.5937/tehnika2101050m.

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This paper presents grey wolf optimization - GWO. After presenting the biological basis of GWO, it explains the method itself and then the main algorithms of the GWO method as well as their mathematical models. The Grey Wolf Algorithm (GWO) is presented in detail as well as the manner of its operation and it application to optimization examples of engineering problems, such as: optimization of speed reducer, pressure vessel, spring, car side impact, cone coupling and cantilever beam. At the end, the results obtained by the GWO method are compared to the results previously obtained by other met

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13

Daru Kusuma, Purba, and Meta Kallista. "Guided imitation optimizer: a metaheuristic combining guided search and imitation search." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 4217. http://dx.doi.org/10.11591/ijai.v13.i4.pp4217-4228.

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This paper proposes a novel metaphor-free metaheuristic, namely the guided imitation optimizer (GIO). This metaheuristic combines the guided search and imitation-based search. There are five guided searches and three imitation based searches. Meanwhile, there are three references used in this metaheuristic: global finest, a randomly picked solution among the swarm, and a randomized solution within the search space. GIO is then evaluated by using 23 classic functions that consist of seven high dimension unimodal functions (HDUF), six high dimension multimodal functions (HDMF), and ten fixed dim

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Purba, Daru Kusuma, and Kallista Meta. "Guided imitation optimizer: a metaheuristic combining guided search and imitation search." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 4217–28. https://doi.org/10.11591/ijai.v13.i4.pp4217-4228.

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This paper proposes a novel metaphor-free metaheuristic, namely the guided imitation optimizer (GIO). This metaheuristic combines the guided search and imitation-based search. There are five guided searches and three imitation based searches. Meanwhile, there are three references used in this metaheuristic: global finest, a randomly picked solution among the swarm, and a randomized solution within the search space. GIO is then evaluated by using 23 classic functions that consist of seven high dimension unimodal functions (HDUF), six high dimension multimodal functions (HDMF), and ten fixed dim

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15

Mary, J. Magelin, and D. I. George Amalarethinam. "A Hybrid Model of Gray Wolf Optimization (GWO) and Modified Sunflower Optimization Algorithm (MSOA) for Efficient Task Scheduling in Cloud Computing." Indian Journal Of Science And Technology 18, no. 23 (2025): 1825–37. https://doi.org/10.17485/ijst/v18i23.927.

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Objective: A hybrid GWO-MSOA model is developed for the task scheduling problem. It efficiently approximates solutions for the multi-objective task scheduling problem in a cloud environment. The model optimizes resource utilization, reduces execution time and cost, and improves overall system performance. Method: This study combines Gray Wolf Optimization (GWO) and Modified Sunflower Optimization Algorithm (MSOA) for efficient task scheduling in cloud. The GWO-MSOA methodology enhances convergence speed and identifies near-optimal solution. The proposed hybrid model efficiently schedules the t

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Wang, Bo, Muhammad Shahzad, Xianglin Zhu, Khalil Ur Rehman, and Saad Uddin. "A Non-linear Model Predictive Control Based on Grey-Wolf Optimization Using Least-Square Support Vector Machine for Product Concentration Control in l-Lysine Fermentation." Sensors 20, no. 11 (2020): 3335. http://dx.doi.org/10.3390/s20113335.

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l-Lysine is produced by a complex non-linear fermentation process. A non-linear model predictive control (NMPC) scheme is proposed to control product concentration in real time for enhancing production. However, product concentration cannot be directly measured in real time. Least-square support vector machine (LSSVM) is used to predict product concentration in real time. Grey-Wolf Optimization (GWO) algorithm is used to optimize the key model parameters (penalty factor and kernel width) of LSSVM for increasing its prediction accuracy (GWO-LSSVM). The proposed optimal prediction model is used

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Wang, Ranran, Jun Zhang, Yijun Lu, and Jiandong Huang. "Towards Designing Durable Sculptural Elements: Ensemble Learning in Predicting Compressive Strength of Fiber-Reinforced Nano-Silica Modified Concrete." Buildings 14, no. 2 (2024): 396. http://dx.doi.org/10.3390/buildings14020396.

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Fiber-reinforced nano-silica concrete (FrRNSC) was applied to a concrete sculpture to address the issue of brittle fracture, and the primary objective of this study was to explore the potential of hybridizing the Grey Wolf Optimizer (GWO) with four robust and intelligent ensemble learning techniques, namely XGBoost, LightGBM, AdaBoost, and CatBoost, to anticipate the compressive strength of fiber-reinforced nano-silica concrete (FrRNSC) for sculptural elements. The optimization of hyperparameters for these techniques was performed using the GWO metaheuristic algorithm, enhancing accuracy throu

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18

Aly, Mohammed, and Abdullah Shawan Alotaibi. "Hybrid Butterfly-Grey Wolf Optimization (HB-GWO): A Novel Metaheuristic Approach for Feature Selection in High-Dimensional Data." Statistics, Optimization & Information Computing 13, no. 6 (2025): 2575–600. https://doi.org/10.19139/soic-2310-5070-2617.

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Feature selection is a critical preprocessing step in high-dimensional data analysis, aiming to enhance model performance by eliminating irrelevant and redundant features. This paper introduces a novel hybrid metaheuristic algorithm, the Hybrid Butterfly-Grey Wolf Optimization (HB-GWO), which synergizes the global exploration capabilities of the Butterfly Optimization Algorithm (BOA) with the local exploitation strengths of the Grey Wolf Optimizer (GWO) to achieve an effective balance between exploration and exploitation in feature selection tasks. The algorithm incorporates an adaptive switch

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Tumari, Mohd Zaidi Mohd, Mohd Muzaffar Zahar, and Mohd Ashraf Ahmad. "Optimal tuning of a wind plant energy production based on improved grey wolf optimizer." Bulletin of Electrical Engineering and Informatics 10, no. 1 (2021): 23–30. http://dx.doi.org/10.11591/eei.v10i1.2509.

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The tuning of optimal controller parameters in wind plant is crucial in order to minimize the effect of wake interaction between turbines. The purpose of this paper is to develop an improved grey wolf optimizer (I-GWO) in order to tune the controller parameters of the turbines so that the total energy production of a wind plant is increased. The updating mechanism of original GWO is modified to improve the efficiency of exploration and exploitation phase while avoiding trapping in local minima solution. A row of ten turbines is considered to evaluate the effectiveness of the I-GWO by maximizin

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Mohd, Zaidi Mohd Tumari, Muzaffar Zahar Mohd, and Ashraf Ahmad Mohd. "Optimal tuning of a wind plant energy production based on improved grey wolf optimizer." Bulletin of Electrical Engineering and Informatics 10, no. 1 (2021): 23–30. https://doi.org/10.11591/eei.v10i1.2509.

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The tuning of optimal controller parameters in wind plant is crucial in order to minimize the effect of wake interaction between turbines. The purpose of this paper is to develop an improved grey wolf optimizer (I-GWO) in order to tune the controller parameters of the turbines so that the total energy production of a wind plant is increased. The updating mechanism of original GWO is modified to improve the efficiency of exploration and exploitation phase while avoiding trapping in local minima solution. A row of ten turbines is considered to evaluate the effectiveness of the I-GWO by maximizin

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Abdel-Basset, Mohamed, Reda Mohamed, Karam M. Sallam, and Ripon K. Chakrabortty. "Light Spectrum Optimizer: A Novel Physics-Inspired Metaheuristic Optimization Algorithm." Mathematics 10, no. 19 (2022): 3466. http://dx.doi.org/10.3390/math10193466.

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This paper introduces a novel physical-inspired metaheuristic algorithm called “Light Spectrum Optimizer (LSO)” for continuous optimization problems. The inspiration for the proposed algorithm is the light dispersions with different angles while passing through rain droplets, causing the meteorological phenomenon of the colorful rainbow spectrum. In order to validate the proposed algorithm, three different experiments are conducted. First, LSO is tested on solving CEC 2005, and the obtained results are compared with a wide range of well-regarded metaheuristics. In the second experiment, LSO is

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Gao, Zheng-Ming, and Juan Zhao. "An Improved Grey Wolf Optimization Algorithm with Variable Weights." Computational Intelligence and Neuroscience 2019 (June 2, 2019): 1–13. http://dx.doi.org/10.1155/2019/2981282.

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With a hypothesis that the social hierarchy of the grey wolves would be also followed in their searching positions, an improved grey wolf optimization (GWO) algorithm with variable weights (VW-GWO) is proposed. And to reduce the probability of being trapped in local optima, a new governing equation of the controlling parameter is also proposed. Simulation experiments are carried out, and comparisons are made. Results show that the proposed VW-GWO algorithm works better than the standard GWO, the ant lion optimization (ALO), the particle swarm optimization (PSO) algorithm, and the bat algorithm

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Yang, Can-Ming, Ye Liu, Yi-Ting Wang, et al. "A Novel Adaptive Kernel Picture Fuzzy C-Means Clustering Algorithm Based on Grey Wolf Optimizer Algorithm." Symmetry 14, no. 7 (2022): 1442. http://dx.doi.org/10.3390/sym14071442.

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Over the years, research on fuzzy clustering algorithms has attracted the attention of many researchers, and they have been applied to various areas, such as image segmentation and data clustering. Various fuzzy clustering algorithms have been put forward based on the initial Fuzzy C-Means clustering (FCM) with Euclidean distance. However, the existing fuzzy clustering approaches ignore two problems. Firstly, clustering algorithms based on Euclidean distance have a high error rate, and are more sensitive to noise and outliers. Secondly, the parameters of the fuzzy clustering algorithms are har

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Weickmann, Klaus, and Edward Berry. "The Tropical Madden–Julian Oscillation and the Global Wind Oscillation." Monthly Weather Review 137, no. 5 (2009): 1601–14. http://dx.doi.org/10.1175/2008mwr2686.1.

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Abstract The global wind oscillation (GWO) is a subseasonal phenomenon encompassing the tropical Madden–Julian oscillation (MJO) and midlatitude processes like meridional momentum transports and mountain torques. A phase space is defined for the GWO following the approach of Wheeler and Hendon for the MJO. In contrast to the oscillatory behavior of the MJO, two red noise processes define the GWO. The red noise spectra have variance at periods that bracket 30–60 or 30–80 days, which are bands used to define the MJO. The correlation between the MJO and GWO is ∼0.5 and cross spectra show well-def

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Yan, Bo, Xu Yang Zhao, Na Xu, Yu Chen, and Wen Bo Zhao. "A Grey Wolf Optimization-based Track-Before-Detect Method for Maneuvering Extended Target Detection and Tracking." Sensors 19, no. 7 (2019): 1577. http://dx.doi.org/10.3390/s19071577.

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A grey wolf optimization-based track-before-detect (GWO-TBD) method is developed for extended target detection and tracking. The aim of the GWO-TBD is tracking weak and maneuvering extended targets in a cluttered environment using the measurement points of an air surveillance radar. The optimal solution is the trajectory constituted by the points of an extended target. At the beginning of the GWO-TBD, the measurements of each scan are clustered into alternative sets. Secondly, closely sets are associated for tracklets. Each tracklet equals a candidate solution. Thirdly, the tracklets are furth

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Yetkin, M., and O. Bilginer. "On the application of nature-inspired grey wolf optimizer algorithm in geodesy." Journal of Geodetic Science 10, no. 1 (2020): 48–52. http://dx.doi.org/10.1515/jogs-2020-0107.

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AbstractNowadays, solving hard optimization problems using metaheuristic algorithms has attracted bountiful attention. Generally, these algorithms are inspired by natural metaphors. A novel metaheuristic algorithm, namely Grey Wolf Optimization (GWO), might be applied in the solution of geodetic optimization problems. The GWO algorithm is based on the intelligent behaviors of grey wolves and a population based stochastic optimization method. One great advantage of GWO is that there are fewer control parameters to adjust. The algorithm mimics the leadership hierarchy and hunting mechanism of gr

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Makhija, Divya, Posham Bhargava Reddy, Chapram Sudhakar, and Varsha Kumari. "Workflow Scheduling in Cloud Computing Environment by Combining Particle Swarm Optimization and Grey Wolf Optimization." Computer Science & Engineering: An International Journal 12, no. 6 (2022): 01–10. http://dx.doi.org/10.5121/cseij.2022.12601.

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Scheduling workflows is a vital challenge in cloud computing due to its NP-complete nature and if an efficient workflow task scheduling algorithm is not used then it affects the system’s overall performance. Therefore, there is a need for an efficient workflow task scheduling algorithm that can distribute dependent tasks to virtual machines efficiently. In this paper, a hybrid workflow task scheduling algorithm based on a combination of Particle Swarm Optimization and Grey Wolf Optimization (PSO GWO) algorithms, is proposed. PSO GWO overcomes the disadvantages of both PSO and GWO algorithms by

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Issa Mohsin, Asma, Asaad S. Daghal, and Adheed Hasan Sallomi. "A beamforming study of the linear antenna array using grey wolf optimization algorithm." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 3 (2020): 1538. http://dx.doi.org/10.11591/ijeecs.v20.i3.pp1538-1546.

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<p><br />The grey wolf optimization (GWO) algorithm is considered an inspired meta-heuristic algorithm, which inspired by the social hierarchy and hunting behavior of the grey wolves. GWO has a high-performance capability of solving constrained, as well as unconstrained optimization problems. In this paper, the beamforming of smart antennas in a code division multiple access system based on the GWO algorithm is investigated. The sidelobe level (SLL) is minimized along with peak sidelobe level reduction, as well as an optimal beam pattern has been accomplished by using GWO to unifor

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Santosa, Paulus Insap, and Ricardus Anggi Pramunendar. "A Robust Feature Construction for Fish Classification Using Grey Wolf Optimizer." Cybernetics and Information Technologies 22, no. 4 (2022): 152–66. http://dx.doi.org/10.2478/cait-2022-0045.

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Abstract The low quality of the collected fish image data directly from its habitat affects its feature qualities. Previous studies tended to be more concerned with finding the best method rather than the feature quality. This article proposes a new fish classification workflow using a combination of Contrast-Adaptive Color Correction (NCACC) image enhancement and optimization-based feature construction called Grey Wolf Optimizer (GWO). This approach improves the image feature extraction results to obtain new and more meaningful features. This article compares the GWO-based and other optimizat

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Mohsin, Asma Issa, Asaad. S. Daghal, and Adheed Hasan Sallomi. "A beamforming study of the linear antenna array using grey wolf optimization algorithm." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 3 (2020): 1538–46. https://doi.org/10.11591/ijeecs.v20.i3.pp1538-1546.

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The grey wolf optimization (GWO) algorithm is considered an inspired meta-heuristic algorithm, which inspired by the social hierarchy and hunting behavior of the grey wolves. GWO has a high-performance capability of solving constrained, as well as unconstrained optimization problems. In this paper, the beamforming of smart antennas in a code division multiple access system based on the GWO algorithm is investigated. The sidelobe level (SLL) is minimized along with peak sidelobe level reduction, as well as an optimal beam pattern has been accomplished by using GWO to uniform linear antenna arra

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Li, Tan, Lin Li, and Zhicheng Liu. "Time Course Changes of the Mechanical Properties of the Iris Pigment Epithelium in a Rat Chronic Ocular Hypertension Model." BioMed Research International 2018 (October 21, 2018): 1–10. http://dx.doi.org/10.1155/2018/4862309.

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Background. The flow field of aqueous humor correlates to the stiffness of iris pigment epithelium (IPE) which acts as a wall of posterior chamber. We focus on the variations of IPE stiffness in a rat ocular hypertension (OHT) model, so as to prepare for exploring the mechanism of duration of OHT. Methods. Episcleral venous cauterization (EVC) was applied on one eye of male adult Sprague-Dawley rats to induce chronic high intraocular pressure. According to the duration of OHT (0, 1, 2, 4, and 8 weeks), rats were randomly divided into Gw0, Gw1, Gw2, Gw4, and Gw8. Atomic force microscope (AFM) a

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Uppalakkal, Vishnu, Venkatesh Ambati, and Rajesh Nair. "Performance Assessment of Metaheuristic Algorithms: Firefly, Grey Wolf, and Moth Flame in Coal Pyrolysis Kinetic Parameter Estimation." International Journal of Mathematical, Engineering and Management Sciences 9, no. 1 (2024): 23–48. http://dx.doi.org/10.33889/ijmems.2024.9.1.002.

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This study investigates the effectiveness of the Firefly Optimizer (FFA), Grey Wolf Optimizer (GWO), and Moth Flame Optimizer (MFO) metaheuristic algorithms in estimating the kinetic parameters of a single-step coal pyrolysis model. By examining the effects of the algorithmic configuration, the initial parameter estimates, and the search space size on the efficacy and efficiency of the optimization run, the research seeks to encourage the qualified engineering application of these algorithms in the field of pyrolysis modeling. Four critical analyses were conducted: convergence efficiency, robu

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Lan, Pu, Kewen Xia, Yongke Pan, and Shurui Fan. "An Improved GWO Algorithm Optimized RVFL Model for Oil Layer Prediction." Electronics 10, no. 24 (2021): 3178. http://dx.doi.org/10.3390/electronics10243178.

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In this study, a model based on the improved grey wolf optimizer (GWO) for optimizing RVFL is proposed to enable the problem of poor accuracy of Oil layer prediction due to the randomness of the parameters present in the random vector function link (RVFL) model to be addressed. Firstly, GWO is improved based on the advantages of chaos theory and the marine predator algorithm (MPA) to overcome the problem of low convergence accuracy in the optimization process of the GWO optimization algorithm. The improved GWO algorithm was then used to optimize the input weights and implicit layer biases of t

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Jabbar, Ayad Mohammed, and Ku Ruhana Ku-Mahamud. "Grey wolf optimization algorithm for hierarchical document clustering." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (2021): 1744. http://dx.doi.org/10.11591/ijeecs.v24.i3.pp1744-1758.

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In data mining, the application of grey wolf optimization (GWO) algorithm has been used in several learning approaches because of its simplicity in adapting to different application domains. Most recent works that concern unsupervised learning have focused on text clustering, where the GWO algorithm shows promising results. Although GWO has great potential in performing text clustering, it has limitations in dealing with outlier documents and noise data. This research introduces medoid GWO (M-GWO) algorithm, which incorporates a medoid recalculation process to share the information of medoids

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Saxena, Prerna, and Ashwin Kothari. "Optimal Pattern Synthesis of Linear Antenna Array Using Grey Wolf Optimization Algorithm." International Journal of Antennas and Propagation 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/1205970.

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The aim of this paper is to introduce the grey wolf optimization (GWO) algorithm to the electromagnetics and antenna community. GWO is a new nature-inspired metaheuristic algorithm inspired by the social hierarchy and hunting behavior of grey wolves. It has potential to exhibit high performance in solving not only unconstrained but also constrained optimization problems. In this work, GWO has been applied to linear antenna arrays for optimal pattern synthesis in the following ways: by optimizing the antenna positions while assuming uniform excitation and by optimizing the antenna current ampli

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Junian, Wahyu Eko, and Hendra Grandis. "HYBRID PARTICLE SWARM OPTIMIZATION AND GREY WOLF OPTIMIZER ALGORITHM FOR CONTROLLED SOURCE AUDIO-FREQUENCY MAGNETOTELLURICS (CSAMT) ONE-DIMENSIONAL INVERSION MODELLING." Rudarsko-geološko-naftni zbornik 38, no. 3 (2023): 65–80. http://dx.doi.org/10.17794/rgn.2023.3.6.

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The Controlled Source Audio-frequency Magnetotellurics (CSAMT) is a geophysical method utilizing artificial electromagnetic signal source to estimate subsurface resistivity structures. One-dimensional (1D) inversion modelling of CSAMT data is non-linear and the solution can be estimated by using global optimization algorithms. Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) are well-known population-based algorithms having relatively simple mathematical formulation and implementation. Hybridization of PSO and GWO algorithms (called hybrid PSO-GWO) can improve the convergence ca

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Jabbar, Ayad Mohammed, and Ku Ruhana Ku-Mahamud. "Grey wolf optimization algorithm for hierarchical document clustering." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (2021): 1744–58. https://doi.org/10.11591/ijeecs.v24.i3.pp1744-1758.

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In data mining, the application of grey wolf optimization (GWO) algorithm has been used in several learning approaches because of its simplicity in adapting to different application domains. Most recent works that concern unsupervised learning have focused on text clustering, where the GWO algorithm shows promising results. Although GWO has great potential in performing text clustering, it has limitations in dealing with outlier documents and noise data. This research introduces medoid GWO (M-GWO) algorithm, which incorporates a medoid recalculation process to share the information of medoids

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G. Krishna Mohan, Dr, Ms N. Sai Prasanna, Mr K. Siva Sai Krishna, and Mr I. Vamsi Krishna. "Applying Distribution Functions to GWO Algorithm." International Journal of Engineering & Technology 7, no. 2.32 (2018): 192. http://dx.doi.org/10.14419/ijet.v7i2.32.15565.

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GWO is an Optimization algorithm. It depends on the different distribution functions. The features of Optimization algorithm are as follows Convergence, precision, and performance. These Characters will generalize this optimization algorithm. In this paper, we explored GWO algorithm for different distributing functions. There are many distribution functions that are kept practical to the GWO algorithm. We evaluated three different distribution functions which are the Gold Stein function, Beale function and the Booth function. To show the effectiveness of the GWO algorithm we have used the abov

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Zainal, Nurezayana, Mohanavali Sithambranathan, Umar Farooq Khattak, Azlan Mohd Zain, Salama A. Mostafa, and Ashanira Mat Deris. "Optimization of Electrical Discharge Machining Process by Metaheuristic Algorithms." Qubahan Academic Journal 4, no. 1 (2024): 277–89. http://dx.doi.org/10.48161/qaj.v4n1a465.

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Because of its versatility and ability to work with difficult materials, Electrical Discharge Machining (EDM) has become an essential tool in many different industries. It can produce precise shapes and intricate details. EDM has transformed fabrication processes in a variety of industries, including aerospace and electronics, medical implants and surgical instruments, and the shaping of small components. Its capacity to machine undercuts and deep cavities with little material removal makes it ideal for producing complex geometries that would be challenging or impossible to accomplish with con

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Mabdeh, Ali Nouh, Rajendran Shobha Ajin, Seyed Vahid Razavi-Termeh, Mohammad Ahmadlou, and A’kif Al-Fugara. "Enhancing the Performance of Machine Learning and Deep Learning-Based Flood Susceptibility Models by Integrating Grey Wolf Optimizer (GWO) Algorithm." Remote Sensing 16, no. 14 (2024): 2595. http://dx.doi.org/10.3390/rs16142595.

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Flooding is a recurrent hazard occurring worldwide, resulting in severe losses. The preparation of a flood susceptibility map is a non-structural approach to flood management before its occurrence. With recent advances in artificial intelligence, achieving a high-accuracy model for flood susceptibility mapping (FSM) is challenging. Therefore, in this study, various artificial intelligence approaches have been utilized to achieve optimal accuracy in flood susceptibility modeling to address this challenge. By incorporating the grey wolf optimizer (GWO) metaheuristic algorithm into various models

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Wan, Yihao, Mingxuan Mao, Lin Zhou, Qianjin Zhang, Xinze Xi, and Chen Zheng. "A Novel Nature-Inspired Maximum Power Point Tracking (MPPT) Controller Based on SSA-GWO Algorithm for Partially Shaded Photovoltaic Systems." Electronics 8, no. 6 (2019): 680. http://dx.doi.org/10.3390/electronics8060680.

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To overcome the real-time problem of maximum power point tracking (MPPT) for partially shaded photovoltaic (PV) systems, a novel nature-inspired MPPT controller with fast convergence and high accuracy is proposed in this paper. The proposed MPPT controller is achieved by combining salp swarm algorithm (SSA) with grey wolf optimizer (GWO) (namely, SSA-GWO). The leader structure of the GWO algorithm is introduced into the basic SSA algorithm to enhance the global search capability. Numerical simulation on 13 benchmark functions was done to evaluate the proposed SSA-GWO algorithm. Finally, the MP

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Adedeji, Wasiu Oyediran, Mofoluwaso Kehinde Adeniran, Kasali Aderinmoye Adedeji, John Rajan, Sunday Ayoola Oke, and Elkanah Olaosebikan Oyetunji. "Optimization of the Wire Electric Discharge Machining Process of Nitinol-60 Shape Memory Alloy Using Taguchi-Pareto Design of Experiments, Grey-Wolf Analysis, and Desirability Function Analysis." IJIEM - Indonesian Journal of Industrial Engineering and Management 4, no. 1 (2023): 28. http://dx.doi.org/10.22441/ijiem.v4i1.18087.

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The nitinol-60 shape memory alloy has been rated as the most widely utilized material in real-life industrial applications, including biomedical appliances, coupling and sealing elements, and activators, among others. However, less is known about its optimization characteristics while taking advantage to choose the best parameter in a surface integrity analysis using the wire EDM process. In this research, the authors proposed a robust Taguchi-Pareto (TP)-grey wolf optimization (GWO)-desirability function analysis (DFA) scheme that hybridizes the TP method, GWO approach, and DFA method. The po

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Chakraborty, Sayan, Ratika Pradhan, Amira S. Ashour, Luminita Moraru, and Nilanjan Dey. "Grey-Wolf-Based Wang’s Demons for Retinal Image Registration." Entropy 22, no. 6 (2020): 659. http://dx.doi.org/10.3390/e22060659.

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Image registration has an imperative role in medical imaging. In this work, a grey-wolf optimizer (GWO)-based non-rigid demons registration is proposed to support the retinal image registration process. A comparative study of the proposed GWO-based demons registration framework with cuckoo search, firefly algorithm, and particle swarm optimization-based demons registration is conducted. In addition, a comparative analysis of different demons registration methods, such as Wang’s demons, Tang’s demons, and Thirion’s demons which are optimized using the proposed GWO is carried out. The results es

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Wang, Chaoming, Anqing Fu, Weidong Li, Mingxing Li, and Tingshu Chen. "Intelligent Identification of Hidden Dangers in Hydrogen Pipeline Transmission Station Using GWO-Optimized Apriori Algorithm." Energies 17, no. 18 (2024): 4539. http://dx.doi.org/10.3390/en17184539.

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This work proposes an intelligent grey-wolf-optimizer-improved Apriori algorithm (GWO-Apriori) to mine the association rules of hidden dangers in hydrogen pipeline transmission stations. The optimal minimum support and minimum confidence are determined by GWO instead of the time-consuming trial approach. Experiments show that the average support and average confidence of association rules using GWO-Apriori increase by 29.8% and 21.3%, respectively, when compared with traditional Apriori. Overall, 59 ineffective association rules out of the total 105 rules are filtered by GWO, which dramaticall

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Elaiyaraja, K., and M. Senthil Kumar. "A Novel Variable Weight Grey Wolf Optimization Algorithm in Medical Image Fusion." Journal of Medical Imaging and Health Informatics 11, no. 5 (2021): 1501–8. http://dx.doi.org/10.1166/jmihi.2021.3475.

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Medical image fusion (MIF) is essential in clinical domain that integrates the multi-modal medical features to a unique frame known as fused image which finds utility in diagnosis process. Scaling based approaches are the commonly used multimodal MIF model where the generalized scaling has a stationary scale value selection that enhances the fusion quality Discrete Wavelet Transform (db4)-based approaches give a maximum amount of approximation in multi-modal medical image fusion, while using less edge features. For generating efficient edge features, Laplacian filtering (LF) approach is employ

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Bouhadouza, Boubekeur, Fares Sadaoui, and Abdelkader Boukaroura. "Performance of PSO-GWO, VAGWO, and GWO optimization techniques for economic dispatch problem: studied case of the southeast algerian electrical network." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 2 (2024): e10179. http://dx.doi.org/10.54021/seesv5n2-437.

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Economic dispatch (ED) aims to identify the most cost-effective strategy for allocating power generation while meeting demand and adhering to the physical constraints of the power system. In this paper, three algorithms are proposed to solve the ED problem in power systems, including a hybrid Particle Swarm Optimization with Grey Wolf Optimizer (PSO-GWO), modified Velocity Aided Grey Wolf Optimizer (VAGWO), and Grey Wolf Optimizer (GWO). PSO is a meta-heuristic optimization technique designed to find the optimal solution to a problem by guiding the movement of particles within a defined explor

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Tang, Kai, Jiahui Li, MengTing Yang, Xinyi Yang, Junxiong Feng, and Suhang Liu. "A Hybrid Model Based on Grey Wolf Optimizer and Lagrangian Support Vector Regression for European Natural Gas Consumption Forecasting." Journal of Energy Research and Reviews 13, no. 2 (2023): 11–19. http://dx.doi.org/10.9734/jenrr/2023/v13i2258.

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Natural gas plays an important role in industry as a clean energy, with the intensification of the Russia-Ukraine war, there is a large-scale energy shortage in Europe, and the natural gas supply in Europe has a natural gas crisis due to the cut-off of the Nord Stream No.1 pipeline. Therefore, it is necessary to accurately predict the consumption of natural gas. In order to fulfill this requirement, this paper uses the Lagrangian Support Vector Regression model with Sorensen kernel based on the Nonlinear Auto-Regressive model and Grey Wolf Optimizer for 5-step forecasting of monthly natural ga

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Nsour, Heba Al, Mohammed Alweshah, Abdelaziz I. Hammouri, Hussein Al Ofeishat, and Seyedali Mirjalili. "A Hybrid Grey Wolf Optimiser Algorithm for Solving Time Series Classification Problems." Journal of Intelligent Systems 29, no. 1 (2018): 846–57. http://dx.doi.org/10.1515/jisys-2018-0129.

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Abstract One of the major objectives of any classification technique is to categorise the incoming input values based on their various attributes. Many techniques have been described in the literature, one of them being the probabilistic neural network (PNN). There were many comparisons made between the various published techniques depending on their precision. In this study, the researchers investigated the search capability of the grey wolf optimiser (GWO) algorithm for determining the optimised values of the PNN weights. To the best of our knowledge, we report for the first time on a GWO al

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Rezaei, Farshad, Hamid R. Safavi, Mohamed Abd Elaziz, Laith Abualigah, Seyedali Mirjalili, and Amir H. Gandomi. "Diversity-Based Evolutionary Population Dynamics: A New Operator for Grey Wolf Optimizer." Processes 10, no. 12 (2022): 2615. http://dx.doi.org/10.3390/pr10122615.

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Evolutionary Population Dynamics (EPD) refers to eliminating poor individuals in nature, which is the opposite of survival of the fittest. Although this method can improve the median of the whole population of the meta-heuristic algorithms, it suffers from poor exploration capability to handle high-dimensional problems. This paper proposes a novel EPD operator to improve the search process. In other words, as the primary EPD mainly improves the fitness of the worst individuals in the population, and hence we name it the Fitness-Based EPD (FB-EPD), our proposed EPD mainly improves the diversity

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Li, Taiyong, Zijie Qian, and Ting He. "Short-Term Load Forecasting with Improved CEEMDAN and GWO-Based Multiple Kernel ELM." Complexity 2020 (February 25, 2020): 1–20. http://dx.doi.org/10.1155/2020/1209547.

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Short-term load forecasting (STLF) is an essential and challenging task for power- or energy-providing companies. Recent research has demonstrated that a framework called “decomposition and ensemble” is very powerful for energy forecasting. To improve the effectiveness of STLF, this paper proposes a novel approach integrating the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), grey wolf optimization (GWO), and multiple kernel extreme learning machine (MKELM), namely, ICEEMDAN-GWO-MKELM, for STLF, following this framework. The proposed ICEEMDAN-GWO-MKELM

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