Log in

Relevant bibliographies by topics / Grey wolf optimizer (GWO) / Journal articles

To see the other types of publications on this topic, follow the link: Grey wolf optimizer (GWO).

Journal articles on the topic 'Grey wolf optimizer (GWO)'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Grey wolf optimizer (GWO).'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Chen, Yingyu, Shenhua Yang, Yongfeng Suo, and Minjie Zheng. "Ship Track Prediction Based on DLGWO-SVR." Scientific Programming 2021 (September 14, 2021): 1–14. http://dx.doi.org/10.1155/2021/9085617.

Full text

Abstract:

To improve the accuracy of ship track prediction, the improved Grey Wolf Optimizer (GWO) and Support Vector Regression (SVR) models are incorporated for ship track prediction. The hunting strategy of dimensional learning was used to optimize the move search process of GWO and balance exploration and exploitation while maintaining population diversity. Selection and updating procedures keep GWO from being stuck in locally optimal solutions. The optimal parameters obtained by modified GWO were substituted into the SVR model to predict ship trajectory. Dimension Learning Grey Wolf Optimizer and S

APA, Harvard, Vancouver, ISO, and other styles

2

Mat Yasin, Zuhaila, Nur Ashida Salim, Nur Fadilah Ab Aziz, Hasmaini Mohamad, and Norfishah Ab Wahab. "Prediction of solar irradiance using grey wolf Optimizer-Least-Square support vector machine." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 1 (2020): 10. http://dx.doi.org/10.11591/ijeecs.v17.i1.pp10-17.

Full text

Abstract:

<span>Prediction of solar irradiance is important for minimizing energy costs and providing high power quality in a photovoltaic (PV) system. This paper proposes a new technique for prediction of hourly-ahead solar irradiance namely Grey Wolf Optimizer- Least-Square Support Vector Machine (GWO-LSSVM). Least Squares Support Vector Machine (LSSVM) has strong ability to learn a complex nonlinear problems. In GWO-LSSVM, the parameters of LSSVM are optimized using Grey Wolf Optimizer (GWO). GWO algorithm is derived based on the hierarchy of leadership and the grey wolf hunting mechanism in na

APA, Harvard, Vancouver, ISO, and other styles

3

Yue, Zhihang, Sen Zhang, and Wendong Xiao. "A Novel Hybrid Algorithm Based on Grey Wolf Optimizer and Fireworks Algorithm." Sensors 20, no. 7 (2020): 2147. http://dx.doi.org/10.3390/s20072147.

Full text

Abstract:

Grey wolf optimizer (GWO) is a meta-heuristic algorithm inspired by the hierarchy of grey wolves (Canis lupus). Fireworks algorithm (FWA) is a nature-inspired optimization method mimicking the explosion process of fireworks for optimization problems. Both of them have a strong optimal search capability. However, in some cases, GWO converges to the local optimum and FWA converges slowly. In this paper, a new hybrid algorithm (named as FWGWO) is proposed, which fuses the advantages of these two algorithms to achieve global optima effectively. The proposed algorithm combines the exploration abili

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

6

Alzaghoul, Esra F., and Sandi N. Fakhouri. "Collaborative Strategy for Grey Wolf Optimization Algorithm." Modern Applied Science 12, no. 7 (2018): 73. http://dx.doi.org/10.5539/mas.v12n7p73.

Full text

Abstract:

Grey wolf Optimizer (GWO) is one of the well known meta-heuristic algorithm for determining the minimum value among a set of values. In this paper, we proposed a novel optimization algorithm called collaborative strategy for grey wolf optimizer (CSGWO). This algorithm enhances the behaviour of GWO that enhances the search feature to search for more points in the search space, whereas more groups will search for the global minimal points. The algorithm has been tested on 23 well-known benchmark functions and the results are verified by comparing them with state of the art algorithms: Polar part

APA, Harvard, Vancouver, ISO, and other styles

7

Sun, Lijun, Binbin Feng, Tianfei Chen, Dongliang Zhao, and Yan Xin. "Equalized Grey Wolf Optimizer with Refraction Opposite Learning." Computational Intelligence and Neuroscience 2022 (May 11, 2022): 1–18. http://dx.doi.org/10.1155/2022/2721490.

Full text

Abstract:

Grey wolf optimizer (GWO) is a global search algorithm based on grey wolf hunting activity. However, the traditional GWO is prone to fall into local optimum, affecting the performance of the algorithm. Therefore, to solve this problem, an equalized grey wolf optimizer with refraction opposite learning (REGWO) is proposed in this study. In REGWO, the issue about the low swarm population variety of GWO in the late iteration is well overcome by the opposing learning of refraction. In addition, the equilibrium pool strategy reduces the likelihood of wolves going to the local extremum. To investiga

APA, Harvard, Vancouver, ISO, and other styles

8

Şahin, İsmail, Murat Dörterler, and Harun Gokce. "Optimization of Hydrostatic Thrust Bearing Using Enhanced Grey Wolf Optimizer." Mechanics 25, no. 6 (2019): 480–86. http://dx.doi.org/10.5755/j01.mech.25.6.22512.

Full text

Abstract:

The need for precise mechanical and tribological properties of the hydrostatic bearings has made them an interesting study topic for optimisation studies. In this paper, power-loss minimization problems of hydrostatic thrust bearings were solved through Grey Wolf Optimizer (GWO). Grey Wolf Optimizer is a meta-heuristic optimization method standing out with its successful applications in engineering design problems. Power-loss minimization problem of hydrostatic thrust bearings was applied on Grey Wolf Optimizer (GWO) for the first time. The results obtained were evaluated together with the pre

APA, Harvard, Vancouver, ISO, and other styles

9

Silaa, Mohammed Yousri, Oscar Barambones, Mohamed Derbeli, Cristian Napole, and Aissa Bencherif. "Fractional Order PID Design for a Proton Exchange Membrane Fuel Cell System Using an Extended Grey Wolf Optimizer." Processes 10, no. 3 (2022): 450. http://dx.doi.org/10.3390/pr10030450.

Full text

Abstract:

This paper presents a comparison of optimizers for tuning a fractional-order proportional-integral-derivative (FOPID) and proportional-integral-derivative (PID) controllers, which were applied to a DC/DC boost converter. Grey wolf optimizer (GWO) and extended grey wolf optimizer (EGWO) have been chosen to achieve suitable parameters. This strategy aims to improve and optimize a proton exchange membrane fuel cell (PEMFC) output power quality through its link with the boost converter. The model and controllers have been implemented in a MATLAB/SIMULINK environment. This study has been conducted

APA, Harvard, Vancouver, ISO, and other styles

10

Komijani, Hossein, Mojtaba Masoumnezhad, Morteza Mohammadi Zanjireh, and Mahdi Mir. "Robust Hybrid Fractional Order Proportional Derivative Sliding Mode Controller for Robot Manipulator Based on Extended Grey Wolf Optimizer." Robotica 38, no. 4 (2019): 605–16. http://dx.doi.org/10.1017/s0263574719000882.

Full text

Abstract:

SUMMARYThis paper presents a novel robust hybrid fractional order proportional derivative sliding mode controller (HFOPDSMC) for 2-degree of freedom (2-DOF) robot manipulator based on extended grey wolf optimizer (EGWO). Sliding mode controller (SMC) is remarkably robust against the uncertainties and external disturbances and shows the valuable properties of accuracy. In this paper, a new fractional order sliding surface (FOSS) is defined. Integrating the fractional order proportional derivative controller (FOPDC) and a new sliding mode controller (FOSMC), a novel robust controller based on HF

APA, Harvard, Vancouver, ISO, and other styles

11

Rashaideh, Hasan, Ahmad Sawaie, Mohammed Azmi Al-Betar, et al. "A Grey Wolf Optimizer for Text Document Clustering." Journal of Intelligent Systems 29, no. 1 (2018): 814–30. http://dx.doi.org/10.1515/jisys-2018-0194.

Full text

Abstract:

Abstract Text clustering problem (TCP) is a leading process in many key areas such as information retrieval, text mining, and natural language processing. This presents the need for a potent document clustering algorithm that can be used effectively to navigate, summarize, and arrange information to congregate large data sets. This paper encompasses an adaptation of the grey wolf optimizer (GWO) for TCP, referred to as TCP-GWO. The TCP demands a degree of accuracy beyond that which is possible with metaheuristic swarm-based algorithms. The main issue to be addressed is how to split text docume

APA, Harvard, Vancouver, ISO, and other styles

12

Ahmed, Rasel, Amril Nazir, Shuhaimi Mahadzir, Mohammad Shorfuzzaman, and Jahedul Islam. "Niching Grey Wolf Optimizer for Multimodal Optimization Problems." Applied Sciences 11, no. 11 (2021): 4795. http://dx.doi.org/10.3390/app11114795.

Full text

Abstract:

Metaheuristic algorithms are widely used for optimization in both research and the industrial community for simplicity, flexibility, and robustness. However, multi-modal optimization is a difficult task, even for metaheuristic algorithms. Two important issues that need to be handled for solving multi-modal problems are (a) to categorize multiple local/global optima and (b) to uphold these optima till the ending. Besides, a robust local search ability is also a prerequisite to reach the exact global optima. Grey Wolf Optimizer (GWO) is a recently developed nature-inspired metaheuristic algorith

APA, Harvard, Vancouver, ISO, and other styles

13

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

14

Mittal, Nitin, Urvinder Singh, and Balwinder Singh Sohi. "Modified Grey Wolf Optimizer for Global Engineering Optimization." Applied Computational Intelligence and Soft Computing 2016 (2016): 1–16. http://dx.doi.org/10.1155/2016/7950348.

Full text

Abstract:

Nature-inspired algorithms are becoming popular among researchers due to their simplicity and flexibility. The nature-inspired metaheuristic algorithms are analysed in terms of their key features like their diversity and adaptation, exploration and exploitation, and attractions and diffusion mechanisms. The success and challenges concerning these algorithms are based on their parameter tuning and parameter control. A comparatively new algorithm motivated by the social hierarchy and hunting behavior of grey wolves is Grey Wolf Optimizer (GWO), which is a very successful algorithm for solving re

APA, Harvard, Vancouver, ISO, and other styles

15

Ridha, Djamel Mohammedi, Zine Rabie, Mosbah Mustafa, and Arif Salem. "Optimum Network Reconfiguration using Grey Wolf Optimizer." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 5 (2018): 2428–35. https://doi.org/10.12928/TELKOMNIKA.v16i5.10271.

Full text

Abstract:

Distribution system Reconfiguration is the process of changing the topology of the distribution network by opening and closing switches to satisfy a specific objective. It is a complex, combinatorial optimization problem involving a nonlinear objective function and constraints. Grey Wolf Optimizer (GWO) is a recently developed metaheuristic search algorithm inspired by the leadership hierarchy and hunting strategy of grey wolves in nature. The objective of this paper is to determine an optimal network reconfiguration that presents the minimum power losses, considering network constraints, and

APA, Harvard, Vancouver, ISO, and other styles

16

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

17

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

18

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

19

Saman, M. Almufti, B. Ahmad Hawar, B. Marqas Ridwan, and R. Asaad Renas. "Grey wolf optimizer: Overview, modifications and applications." International Research Journal of Science, Technology, Education, and Management 1, no. 1 (2021): 44–56. https://doi.org/10.5281/zenodo.5195644.

Full text

Abstract:

The complexity of real-world problems motivated researchers to innovate efficient problem-solving techniques. Generally natural Inspired, Bio Inspired, Metaheuristics based on evolutionary computation and swarm intelligence algorithms have been frequently used for solving complex, real-world optimization and Non-deterministic polynomial hard (NP-Hard) problems because of their ability to adjust to a variety of conditions. This paper describes Grey Wolf Optimizer (GWO) as a Swarm Based metaheuristic algorithm inspired by the leadership hierarchy and hunting behavior of the grey wolves for solvi

APA, Harvard, Vancouver, ISO, and other styles

20

Al-Imron, Cynthia Novel, Dana Marsetiya Utama, and Shanty Kusuma Dewi. "An Energy-Efficient No Idle Permutations Flow Shop Scheduling Problem Using Grey Wolf Optimizer Algorithm." Jurnal Ilmiah Teknik Industri 21, no. 1 (2022): 1–10. http://dx.doi.org/10.23917/jiti.v21i1.17634.

Full text

Abstract:

Energy consumption has become a significant issue in businesses. It is known that the industrial sector has consumed nearly half of the world's total energy consumption in some cases. This research aims to propose the Grey Wolf Optimizer (GWO) algorithm to minimize energy consumption in the No Idle Permutations Flowshop Problem (NIPFP). The GWO algorithm has four phases: initial population initialization, implementation of the Large Rank Value (LRV), grey wolf exploration, and exploitation. To determine the level of machine energy consumption, this study uses three different speed levels. To i

APA, Harvard, Vancouver, ISO, and other styles

21

Wang, Yang, Chengyu Jin, Qiang Li, et al. "A Dynamic Opposite Learning-Assisted Grey Wolf Optimizer." Symmetry 14, no. 9 (2022): 1871. http://dx.doi.org/10.3390/sym14091871.

Full text

Abstract:

The grey wolf optimization (GWO) algorithm is widely utilized in many global optimization applications. In this paper, a dynamic opposite learning-assisted grey wolf optimizer (DOLGWO) was proposed to improve the search ability. Herein, a dynamic opposite learning (DOL) strategy is adopted, which has an asymmetric search space and can adjust with a random opposite point to enhance the exploitation and exploration capabilities. To validate the performance of DOLGWO algorithm, 23 benchmark functions from CEC2014 were adopted in the numerical experiments. A total of 10 popular algorithms, includi

APA, Harvard, Vancouver, ISO, and other styles

22

Euldji, Rafik, Noureddine Batel, Redha Rebhi, et al. "Optimal Backstepping-FOPID Controller Design for Wheeled Mobile Robot." Journal Européen des Systèmes Automatisés 55, no. 1 (2022): 97–107. http://dx.doi.org/10.18280/jesa.550110.

Full text

Abstract:

A design of an optimal backstepping fractional order proportional integral derivative (FOPID) controller for handling the trajectory tracking problem of wheeled mobile robots (WMR) is examined in this study. Tuning parameters is a challenging task, to overcome this issue a hybrid meta-heuristic optimization algorithm has been utilized. This evolutionary technique is known as the hybrid whale grey wolf optimizer (HWGO), which benefits from the performances of the two traditional algorithms, the whale optimizer algorithm (WOA) and the grey wolf optimizer (GWO), to obtain the most suitable soluti

APA, Harvard, Vancouver, ISO, and other styles

23

Z., M. Yasin, A. Salim N., F. A. Aziz N., M. Ali Y., and Mohamad H. "Long-term load forecasting using grey wolf optimizer -leastsquares support vector mach." International Journal of Artificial Intelligence (IJ-AI) 9, no. 3 (2020): 417–23. https://doi.org/10.11591/ijai.v9.i3.pp417-423.

Full text

Abstract:

Long term load forecasting data is important for grid expansion and power system operation. Besides, it also important to ensure the generation capacity meet electricity demand at all times. In this paper, Least-Square Support Vector Machine (LSSVM) is used to predict the long-term load demand. Four inputs are considered which are peak load demand, ambient temperature, humidity and wind speed. Total load demand is set as the output of prediction in LSSVM. In order to improve the accuracy of the LSSVM, Grey Wolf Optimizer (GWO) is hybridized to obtain the optimal parameters of LSSVM namely GWO-

APA, Harvard, Vancouver, ISO, and other styles

24

Jing-Sen Liu, Jing-Sen Liu, Qing-Qing Liu Jing-Sen Liu, and Fang Zuo Qing-Qing Liu. "A Guided Mutation Grey Wolf Optimizer Algorithm Integrating Flower Pollination Mechanism." 電腦學刊 33, no. 2 (2022): 051–67. http://dx.doi.org/10.53106/199115992022043302005.

Full text

Abstract:

<p>The basic Grey Wolf Optimizer (GWO) has some shortcomings, for example, the convergence speed is slow, it is easy to fall into local extremum, and high-dimensional optimization ability is poor and so on. In response to these shortcomings, an improved grey wolf algorithm which combines flower pollination mechanism, teaching mechanism and polynomial variation is proposed in this study. The flower pollination mechanism is integrated with GWO algorithm, Levy distribution is introduced into the global search of grey wolf population. And the double random mechanism is added in the local sea

APA, Harvard, Vancouver, ISO, and other styles

25

Jing-Sen Liu, Jing-Sen Liu, Qing-Qing Liu Jing-Sen Liu, and Fang Zuo Qing-Qing Liu. "A Guided Mutation Grey Wolf Optimizer Algorithm Integrating Flower Pollination Mechanism." 電腦學刊 33, no. 2 (2022): 051–67. http://dx.doi.org/10.53106/199115992022043302005.

Full text

Abstract:

<p>The basic Grey Wolf Optimizer (GWO) has some shortcomings, for example, the convergence speed is slow, it is easy to fall into local extremum, and high-dimensional optimization ability is poor and so on. In response to these shortcomings, an improved grey wolf algorithm which combines flower pollination mechanism, teaching mechanism and polynomial variation is proposed in this study. The flower pollination mechanism is integrated with GWO algorithm, Levy distribution is introduced into the global search of grey wolf population. And the double random mechanism is added in the local sea

APA, Harvard, Vancouver, ISO, and other styles

26

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

27

Shen, Jiazheng, Tang Sai Hong, Luxin Fan, Ruixin Zhao, Mohd Khairol Anuar b. Mohd Ariffin, and Azizan bin As’arry. "Development of an Improved GWO Algorithm for Solving Optimal Paths in Complex Vertical Farms with Multi-Robot Multi-Tasking." Agriculture 14, no. 8 (2024): 1372. http://dx.doi.org/10.3390/agriculture14081372.

Full text

Abstract:

As the global population grows, achieving Zero Hunger by 2030 presents a significant challenge. Vertical farming technology offers a potential solution, making the path planning of agricultural robots in vertical farms a research priority. This study introduces the Vertical Farming System Multi-Robot Trajectory Planning (VFSMRTP) model. To optimize this model, we propose the Elitist Preservation Differential Evolution Grey Wolf Optimizer (EPDE-GWO), an enhanced version of the Grey Wolf Optimizer (GWO) incorporating elite preservation and differential evolution. The EPDE-GWO algorithm is compar

APA, Harvard, Vancouver, ISO, and other styles

28

Hamdan, Adel, Muhannad Tahboush, Mohammad Adawy, Tariq Alwada’n, Sameh Ghwanmeh, and Moath Husni. "Phishing detection using grey wolf and particle swarm optimizer." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 5 (2024): 5961. http://dx.doi.org/10.11591/ijece.v14i5.pp5961-5969.

Full text

Abstract:

Phishing could be considered a worldwide problem; undoubtedly, the number of illegal websites has increased quickly. Besides that, phishing is a security attack that has several purposes, such as personal information, credit card numbers, and other information. Phishing websites look like legitimate ones, which makes it difficult to differentiate between them. There are several techniques and methods for phishing detection. The authors present two machine-learning algorithms for phishing detection. Besides that, the algorithms employed are XGBoost and random forest. Also, this study uses parti

APA, Harvard, Vancouver, ISO, and other styles

29

Ghalambaz, Mehdi, Reza Jalilzadeh Yengejeh, and Amir Hossein Davami. "Building energy optimization using Grey Wolf Optimizer (GWO)." Case Studies in Thermal Engineering 27 (October 2021): 101250. http://dx.doi.org/10.1016/j.csite.2021.101250.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Marot, Julien, Flora Zidane, Maha El-Abed, Jerome Lanteri, Jean-Yves Dauvignac, and Claire Migliaccio. "GWO-Based Joint Optimization of Millimeter-Wave System and Multilayer Perceptron for Archaeological Application." Sensors 24, no. 9 (2024): 2749. http://dx.doi.org/10.3390/s24092749.

Full text

Abstract:

Recently, low THz radar-based measurement and classification for archaeology emerged as a new imaging modality. In this paper, we investigate the classification of pottery shards, a key enabler to understand how the agriculture was introduced from the Fertile Crescent to Europe. Our purpose is to jointly design the measuring radar system and the classification neural network, seeking the maximal compactness and the minimal cost, both directly related to the number of sensors. We aim to select the least possible number of sensors and place them adequately, while minimizing the false recognition

APA, Harvard, Vancouver, ISO, and other styles

31

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

32

Wong, Lo Ing, Mohd Herwan Sulaiman, and Mohd Rusllim Mohamed. "Solving Economic Dispatch Problems with Practical Constraints Utilizing Grey Wolf Optimizer." Applied Mechanics and Materials 785 (August 2015): 511–15. http://dx.doi.org/10.4028/www.scientific.net/amm.785.511.

Full text

Abstract:

This paper presents the application of a new meta-heuristic called Grey Wolf Optimizer (GWO) which inspired by grey wolves (Canis lupus) for solving economic dispatch (ED) problems. The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, the three main steps of hunting: searching for prey, encircling prey and attacking prey are implemented. In this paper, GWO was demonstrated and tested on two well-known test systems with p

APA, Harvard, Vancouver, ISO, and other styles

33

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

34

Momanyi, Enock, and Davies Segera. "A Master-Slave Binary Grey Wolf Optimizer for Optimal Feature Selection in Biomedical Data Classification." BioMed Research International 2021 (October 12, 2021): 1–12. http://dx.doi.org/10.1155/2021/5556941.

Full text

Abstract:

A new master-slave binary grey wolf optimizer (MSBGWO) is introduced. A master-slave learning scheme is introduced to the grey wolf optimizer (GWO) to improve its ability to explore and get better solutions in a search space. Five high-dimensional biomedical datasets are used to test the ability of MSBGWO in feature selection. The experimental results of MSBGWO are superior in terms of classification accuracy, precision, recall, F -measure, and number of features selected when compared to those of the binary grey wolf optimizer version 2 (BGWO2), binary genetic algorithm (BGA), binary particle

APA, Harvard, Vancouver, ISO, and other styles

35

Zhou, Xingyu, Guoqing Shi, and Jiandong Zhang. "Improved Grey Wolf Algorithm: A Method for UAV Path Planning." Drones 8, no. 11 (2024): 675. http://dx.doi.org/10.3390/drones8110675.

Full text

Abstract:

The Grey Wolf Optimizer (GWO) algorithm is recognized for its simplicity and ease of implementation, and has become a preferred method for solving global optimization problems due to its adaptability and search capabilities. Despite these advantages, existing Unmanned Aerial Vehicle (UAV) path planning algorithms are often hindered by slow convergence rates, susceptibility to local optima, and limited robustness. To surpass these limitations, we enhance the application of GWO in UAV path planning by improving its trajectory evaluation function, convergence factor, and position update method. W

APA, Harvard, Vancouver, ISO, and other styles

36

Rezaei, Farshad, Hamid Reza Safavi, Mohamed Abd Elaziz, Shaker H. Ali El-Sappagh, Mohammed Azmi Al-Betar, and Tamer Abuhmed. "An Enhanced Grey Wolf Optimizer with a Velocity-Aided Global Search Mechanism." Mathematics 10, no. 3 (2022): 351. http://dx.doi.org/10.3390/math10030351.

Full text

Abstract:

This paper proposes a novel variant of the Grey Wolf Optimization (GWO) algorithm, named Velocity-Aided Grey Wolf Optimizer (VAGWO). The original GWO lacks a velocity term in its position-updating procedure, and this is the main factor weakening the exploration capability of this algorithm. In VAGWO, this term is carefully set and incorporated into the updating formula of the GWO. Furthermore, both the exploration and exploitation capabilities of the GWO are enhanced in VAGWO via stressing the enlargement of steps that each leading wolf takes towards the others in the early iterations while st

APA, Harvard, Vancouver, ISO, and other styles

37

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

38

Mustaffa, Zuriani, Mohd Herwan Sulaiman, and Yuhanis Yusof. "An Application of Grey Wolf Optimizer for Commodity Price Forecasting." Applied Mechanics and Materials 785 (August 2015): 473–78. http://dx.doi.org/10.4028/www.scientific.net/amm.785.473.

Full text

Abstract:

Over the recent decades, there are many nature inspired optimization algorithms have been introduced. In this study, a newly algorithm namely Grey Wolf Optimizer (GWO) is employed for gasoline price forecasting. The performance of GWO is compared against the results produced by Artificial Bee Colony (ABC) algorithm and Differential Evolution (DE) algorithm. Measured based on Mean Absolute Percentage Error (MAPE) and prediction accuracy, the GWO is proven to produce significantly better results as compared to the identified algorithms.

APA, Harvard, Vancouver, ISO, and other styles

39

Soban, Soban, R. Sireesha, BP .., Pavithra G., and Soban Badonia. "Grey Wolf Optimizer Algorithm for Multi-Objective Optimal Power Flow." Journal of Intelligent Systems and Internet of Things 12, no. 1 (2024): 20–32. http://dx.doi.org/10.54216/jisiot.120102.

Full text

Abstract:

This article introduces the Grey Wolf Optimizer (GWO) algorithm, a novel method aimed at tackling the challenges posed by the multi-objective Optimal Power Flow (OPF) problem. Drawing inspiration from the foraging behavior of grey wolves, GWO stands apart from traditional approaches by enhancing initial solutions without relying on gradient data collection from the objective function. In the domain of power system optimization, the OPF problem is widely acknowledged, involving constraints related to generator parameters, valve-point loading, reactive power, and active power. The proposed GWO t

APA, Harvard, Vancouver, ISO, and other styles

40

Zhang, Sen, Qifang Luo, and Yongquan Zhou. "Hybrid Grey Wolf Optimizer Using Elite Opposition-Based Learning Strategy and Simplex Method." International Journal of Computational Intelligence and Applications 16, no. 02 (2017): 1750012. http://dx.doi.org/10.1142/s1469026817500122.

Full text

Abstract:

To overcome the poor population diversity and slow convergence rate of grey wolf optimizer (GWO), this paper introduces the elite opposition-based learning strategy and simplex method into GWO, and proposes a hybrid grey optimizer using elite opposition (EOGWO). The diversity of grey wolf population is increased and exploration ability is improved. The experiment results of 13 standard benchmark functions indicate that the proposed algorithm has strong global and local search ability, quick convergence rate and high accuracy. EOGWO is also effective and feasible in both low-dimensional and hig

APA, Harvard, Vancouver, ISO, and other styles

41

Ma, Teng, Fengrong Bi, Xu Wang, et al. "Optimized Fuzzy Skyhook Control for Semi-Active Vehicle Suspension with New Inverse Model of Magnetorheological Fluid Damper." Energies 14, no. 6 (2021): 1674. http://dx.doi.org/10.3390/en14061674.

Full text

Abstract:

To improve the performance of vehicle suspension, this paper proposes a semi-active vehicle suspension with a magnetorheological fluid (MRF) damper. We designed an optimized fuzzy skyhook controller with grey wolf optimizer (GWO) algorithm base on a new neuro-inverse model of the MRF damper. Because the inverse model of the MRF damper is difficult to establish directly, the Elman neural network was applied. The novelty of this study is the application of the new inverse model for semi-active vibration control and optimization of the semi-active suspension control method. The calculation result

APA, Harvard, Vancouver, ISO, and other styles

42

Singh, Narinder, and S. B. Singh. "Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Improving Convergence Performance." Journal of Applied Mathematics 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/2030489.

Full text

Abstract:

A newly hybrid nature inspired algorithm called HPSOGWO is presented with the combination of Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO). The main idea is to improve the ability of exploitation in Particle Swarm Optimization with the ability of exploration in Grey Wolf Optimizer to produce both variants’ strength. Some unimodal, multimodal, and fixed-dimension multimodal test functions are used to check the solution quality and performance of HPSOGWO variant. The numerical and statistical solutions show that the hybrid variant outperforms significantly the PSO and GWO varia

APA, Harvard, Vancouver, ISO, and other styles

43

Singh, Shitu, Anshul Gopal, Susheel Kumar Joshi, and Jagdish Chand Bansal. "StableGWO : A grey wolf optimizer with von Neumann stability criteria." Journal of Information and Optimization Sciences 46, no. 5 (2025): 1401–24. https://doi.org/10.47974/jios-1361.

Full text

Abstract:

The Grey wolf optimizer (GWO) is a recent swarm intelligence-based algorithm. The performance of GWO highly depends on the choice of the controlling parameters. Finding the most suitable values of these parameters becomes a challenging task due to the stochastic nature of the position update process. Mathematical analysis of the position update mechanism can guide in finding the most appropriate range of these parameters. This paper attempts to use von Neumann stability criteria to find the most suitable value of these parameters. The objective of this study is also to check whether the origin

APA, Harvard, Vancouver, ISO, and other styles

44

Tawhid, Mohamed A., and Ahmed F. Ali. "Multidirectional Grey Wolf Optimizer Algorithm for Solving Global Optimization Problems." International Journal of Computational Intelligence and Applications 17, no. 04 (2018): 1850022. http://dx.doi.org/10.1142/s1469026818500220.

Full text

Abstract:

In this paper, we propose a new hybrid population-based meta-heuristics algorithm inspired by grey wolves in order to solve integer programming and minimax problems. The proposed algorithm is called Multidirectional Grey Wolf Optimizer (MDGWO) algorithm. In the proposed algorithm, we try to accelerate the standard grey wolf optimizer algorithm (GWO) by invoking the multidirectional search method with it in order to accelerate the search instead of letting the standard GWO run for more iterations without significant improvement in the results. MDGWO starts the search by applying the standard GW

APA, Harvard, Vancouver, ISO, and other styles

45

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

46

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

47

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

48

Liu, Xinyang, Yifan Wang, and Miaolei Zhou. "Dimensional Learning Strategy-Based Grey Wolf Optimizer for Solving the Global Optimization Problem." Computational Intelligence and Neuroscience 2022 (January 30, 2022): 1–31. http://dx.doi.org/10.1155/2022/3603607.

Full text

Abstract:

Grey wolf optimizer (GWO) is an up-to-date nature-inspired optimization algorithm which has been used for solving many of the real-world applications since it was proposed. In the standard GWO, individuals are guided by the three dominant wolves alpha, beta, and delta in the leading hierarchy of the swarm. These three wolves provide their information about the potential locations of the global optimum in the search space. This learning mechanism is easy to implement. However, when the three wolves are in conflicting directions, an individual may not obtain better knowledge to update its positi

APA, Harvard, Vancouver, ISO, and other styles

49

Barraza, Juan, Luis Rodríguez, Oscar Castillo, Patricia Melin, and Fevrier Valdez. "A New Hybridization Approach between the Fireworks Algorithm and Grey Wolf Optimizer Algorithm." Journal of Optimization 2018 (May 27, 2018): 1–18. http://dx.doi.org/10.1155/2018/6495362.

Full text

Abstract:

The main aim of this paper is to present a new hybridization approach for combining two powerful metaheuristics, one inspired by physics and the other one based on bioinspired phenomena. The first metaheuristic is based on physics laws and imitates the explosion of the fireworks and is called Fireworks Algorithm; the second metaheuristic is based on the behavior of the grey wolf and belongs to swarm intelligence methods, and this method is called the Grey Wolf Optimizer algorithm. For this work we studied and analyzed the advantages of the two methods and we propose to enhance the weakness of

APA, Harvard, Vancouver, ISO, and other styles

50

Nayeem, Golam Moktader, Mingyu Fan, and Golam Moktader Daiyan. "Adaptive Q-Learning Grey Wolf Optimizer for UAV Path Planning." Drones 9, no. 4 (2025): 246. https://doi.org/10.3390/drones9040246.

Full text

Abstract:

Path planning is crucial for safely and efficiently navigating unmanned aerial vehicles (UAVs) toward operational goals. Often, this is a complex, multi-constraint, and non-linear optimization problem, and metaheuristic algorithms are frequently used to solve it. Grey Wolf Optimization (GWO) is one of the most popular algorithms for solving such problems. However, standard GWO has several limitations, such as premature convergence, susceptibility to local minima, and unsuitability for dynamic environments due to its lack of adaptive learning. We propose a Q-learning-based GWO algorithm to addr

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!