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Journal articles on the topic 'GWO TECHNIQUE'

Author: Grafiati

Published: 11 September 2023

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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Mousa, M. E., M. A. Ebrahim, Magdy M. Zaky, E. M. Saied, and S. A. Kotb. "Hybrid Optimization Technique for Enhancing the Stability of Inverted Pendulum System." International Journal of Swarm Intelligence Research 12, no. 1 (2021): 77–97. http://dx.doi.org/10.4018/ijsir.2021010105.

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The inverted pendulum system (IPS) is considered the milestone of many robotic-based industries. In this paper, a new variant of variable structure adaptive fuzzy (VSAF) is used with new reduced linear quadratic regulator (RLQR) and feedforward gain for enhancing the stability of IPS. The optimal determining of VSAF parameters as well as Q and R matrices of RLQR are obtained by using a modified grey wolf optimizer with adaptive constants property via particle swarm optimization technique (GWO/PSO-AC). A comparison between the hybrid GWO/PSO-AC and classical GWO/PSO based on multi-objective fun

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Gupta, Priyank, Sanjay Kumar Gupta, and Rakesh Singh Jadon. "Adaptive Grey Wolf Optimization Technique for Stock Index Price Prediction on Recurring Neural Network Variants." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 11s (2023): 309–18. http://dx.doi.org/10.17762/ijritcc.v11i11s.8103.

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In this paper, we propose a Long short-term memory (LSTM) and Adaptive Grey Wolf Optimization (GWO)--based hybrid model for predicting the stock prices of the Major Indian stock indices, i.e., Sensex. The LSTM is an advanced neural network that handles uncertain, nonlinear, and sequential data. The challenges are its weight and bias optimization. The classical backpropagation has issues of dangling on local minima or overfitting the dataset. Thus, we propose a GWO-based hybrid approach to evolve the weights and biases of the LSTM and the dense layers. We have made the GWO more robust by introd

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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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Nahak, Narayan, and Ranjan Kumar Mallick. "Efficacy of GWO Optimized PI and Lead-lag Controller for Design of UPFC based Supplementary Damping Controller." IAES International Journal of Robotics and Automation (IJRA) 6, no. 4 (2017): 241. http://dx.doi.org/10.11591/ijra.v6i4.pp241-251.

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<p><span>On line tuning of FACTS based damping controller is a vital decisive task in power system. In this regard two things need to be addressed, one is selection of a proper controller and another one is selection of a powerful optimization technique. In this work Grey Wolf Optimizer (GWO) technique is proposed to tune parameters of PI and lead lag controller based on UPFC to damp intra plant and inter area electromechanical oscillations with single and multi machine power system. A broad comparison has been performed with eigen value analysis between optimized PI and lead lag d

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Ajeng, Maharani Putri, Rustam Zuherman, Pandelaki Jacub, Wirasati Ilsya, and Hartini Sri. "Acute sinusitis data classification using grey wolf optimization-based support vector machin." International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (2021): 438–45. https://doi.org/10.11591/ijai.v10.i2.pp438-445.

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Acute sinusitis is the most common form of sinusitis, and it causes swelling and inflammation within the nose. The main thing that can causes sinusitis is probably due to viruses, and also can be caused by other factors, namely bacteria, fungi, irritation, dust, and allergens. In this research, the CT scan data attributes will be used for classification and grey wolf optimization-support vector machine (GWO-SVM) will be the machine learning technique used, where the GWO technique will be used to tuned the parameters in SVM. The performance of methods was analyzed using the python programming l

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Badi, Manjulata, Sheila Mahapatra, Bishwajit Dey, and Saurav Raj. "A Hybrid GWO-PSO Technique for the Solution of Reactive Power Planning Problem." International Journal of Swarm Intelligence Research 13, no. 1 (2022): 1–30. http://dx.doi.org/10.4018/ijsir.2022010104.

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Over the years the optimization in various areas of power system has immensely attracted the attention of power engineers and researchers. RPP problem is one of such areas. This is done by the placement of reactive power sources in the weak buses and thereafter minimizing the operating cost of the system which is directly dependent on the system transmission loss. The work proposed in this article utilizes FVSI method to detect the weak bus. GWO-PSO is proposed in the current work for providing optimal solution to RPP problem. To test the efficacy of the proposed technique, comparative analysi

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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.

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<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

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Sulttan, Mohammed Qasim, Salam Waley Shneen, and Jafaar Mohammed Daif Alkhasraji. "Performance enhancement of large-scale linear dynamic MIMO systems using GWO-PID controller." Bulletin of Electrical Engineering and Informatics 12, no. 5 (2023): 2852–59. http://dx.doi.org/10.11591/eei.v12i5.4870.

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The multi-input multi-output (MIMO) technique is becoming grown and integrated into wireless wideband communication. MIMO techniques suffer from a large-scale linear dynamic problem, it will be easy to adjust the proportional-integral-derivative (PID) of a continuous system, unlike the nonlinear model. This work displays the tuning of the PID controller for MIMO systems utilizing a statistical grey wolf optimization (GWO) and evaluated by objective function as integral time absolute error (ITAE). The instantaneous adjusting characteristic GWO approach is the criterion that distinguishes such a

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Ramana, Ramana, K. Kavitha, Smita Rani Sahu, B. Manideep, T. Ravi Kumar, and Nibedan Panda. "An Improved Chaotic Grey Wolf Optimization Algorithm (CGWO)." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 11s (2023): 341–48. http://dx.doi.org/10.17762/ijritcc.v11i11s.8161.

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Grey Wolf Optimization (GWO) is a new type of swarm-based technique for dealing with realistic engineering design constraints and unconstrained problems in the field of metaheuristic research. Swarm-based techniques are a type of population-based algorithm inspired by nature that can produce low-cost, quick, and dependable solutions to a wider variety of complications. It is the best choice when it can achieve faster convergence by avoiding local optima trapping. This work incorporates chaos theory with the standard GWO to improve the algorithm's performance due to the ergodicity of chaos. The

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Duan, Yonghui, Chen Li, Xiang Wang, Yibin Guo, and Hao Wang. "Forecasting Influenza Trends Using Decomposition Technique and LightGBM Optimized by Grey Wolf Optimizer Algorithm." Mathematics 13, no. 1 (2024): 24. https://doi.org/10.3390/math13010024.

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Influenza is an acute respiratory infectious disease marked by its high contagiousness and rapid spread, caused by influenza viruses. Accurate influenza prediction is a critical issue in public health and serves as an essential tool for epidemiological studies. This paper seeks to improve the prediction accuracy of influenza-like illness (ILI) proportions by proposing a novel predictive model that integrates a data decomposition technique with the Grey Wolf Optimizer (GWO) algorithm, aiming to overcome the limitations of current prediction methods. Firstly, the most suitable indicators were se

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Roy, Provas Kumar, Moumita Pradhan, and Tandra Pal. "Multi-Objective Hydro-Thermal Scheduling Problem Using Two Novel Optimization Techniques." International Journal of Swarm Intelligence Research 12, no. 3 (2021): 1–36. http://dx.doi.org/10.4018/ijsir.2021070101.

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This article describes an efficient and reliable strategy for the scheduling of nonlinear multi-objective hydrothermal power systems using the grey wolf optimization (GWO) technique. Moreover, the theory of oppositional-based learning (OBL) is integrated with original GWO for further enhancing its convergence rate and solution accuracy. The constraints related to hydro and thermal plants and environmental aspects are also considered in this paper. To show its efficiency and effectiveness, the proposed GWO and OGWO algorithms are authenticated for the test system consisting of a multi-chain cas

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Putri, Ajeng Maharani, Zuherman Rustam, Jacub Pandelaki, Ilsya Wirasati, and Sri Hartini. "Acute sinusitis data classification using grey wolf optimization-based support vector machine." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (2021): 438. http://dx.doi.org/10.11591/ijai.v10.i2.pp438-445.

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<span id="docs-internal-guid-ebf19048-7fff-9350-093e-7f1e8df23393"><span>Acute sinusitis is the most common form of sinusitis, and it causes swelling and inflammation within the nose. The main thing that can causes sinusitis is probably due to viruses, and also can be caused by other factors, namely bacteria, fungi, irritation, dust, and allergens. In this research, the CT scan data attributes will be used for classification and grey wolf optimization-support vector machine (GWO-SVM) will be the machine learning technique used, where the GWO technique will be used to tuned the para

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Yan, Fu, Jianzhong Xu, and Kumchol Yun. "Dynamically Dimensioned Search Grey Wolf Optimizer Based on Positional Interaction Information." Complexity 2019 (December 5, 2019): 1–36. http://dx.doi.org/10.1155/2019/7189653.

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The grey wolf optimizer (GWO) algorithm is a recently developed, novel, population-based optimization technique that is inspired by the hunting mechanism of grey wolves. The GWO algorithm has some distinct advantages, such as few algorithm parameters, strong global optimization ability, and ease of implementation on a computer. However, the paramount challenge is that there are some cases where the GWO is prone to stagnation in local optima. This drawback of the GWO algorithm may be attributed to an insufficiency in its position-updated equation, which disregards the positional interaction inf

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Shetty, Divya, and Jayalakshmi Narayana Sabhahit. "Grey wolf optimization and incremental conductance based hybrid MPPT technique for solar powered induction motor driven water pump." International Journal of Renewable Energy Development 13, no. 1 (2023): 52–61. http://dx.doi.org/10.14710/ijred.2024.57096.

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The use of Solar Powered Water Pumps (SPWP) has emerged as a significant advancement in irrigation systems, offering a viable alternative to electricity and diesel-based pumping methods. The appeal of SPWPs to farmers lies in their low maintenance costs and the incentives provided by government agencies to support sustainable and cost-effective agricultural practices. However, a critical challenge faced by solar photovoltaic (PV) systems is their susceptibility to power loss under partial shading conditions, which can persist for extended periods, ultimately reducing system efficiency. To addr

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Oliveira, Josenalde, Paulo Moura Oliveira, José Boaventura-Cunha, and Tatiana Pinho. "Evaluation of Hunting-Based Optimizers for a Quadrotor Sliding Mode Flight Controller." Robotics 9, no. 2 (2020): 22. http://dx.doi.org/10.3390/robotics9020022.

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The design of Multi-Input Multi-Output nonlinear control systems for a quadrotor can be a difficult task. Nature inspired optimization techniques can greatly improve the design of non-linear control systems. Two recently proposed hunting-based swarm intelligence inspired techniques are the Grey Wolf Optimizer (GWO) and the Ant Lion Optimizer (ALO). This paper proposes the use of both GWO and ALO techniques to design a Sliding Mode Control (SMC) flight system for tracking improvement of altitude and attitude in a quadrotor dynamic model. SMC is a nonlinear technique which requires that its stri

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Abdelkhalek, Maha, Souheib Ben Amor, and Sofiène Affes. "Data-Aided Maximum Likelihood Joint Angle and Delay Estimator Over Orthogonal Frequency Division Multiplex Single-Input Multiple-Output Channels Based on New Gray Wolf Optimization Embedding Importance Sampling." Sensors 24, no. 17 (2024): 5821. http://dx.doi.org/10.3390/s24175821.

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In this paper, we propose a new data-aided (DA) joint angle and delay (JADE) maximum likelihood (ML) estimator. The latter consists of a substantially modified and, hence, significantly improved gray wolf optimization (GWO) technique by fully integrating and embedding within it the powerful importance sampling (IS) concept. This new approach, referred to hereafter as GWOEIS (for “GWO embedding IS”), guarantees global optimality, and offers higher resolution capabilities over orthogonal frequency division multiplex (OFDM) (i.e., multi-carrier and multi-path) single-input multiple-output (SIMO)

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Mohd, Razeef, Muheet Ahmed Butt, and Majid Zaman Baba. "Grey Wolf-Based Linear Regression Model for Rainfall Prediction." International Journal of Information Technologies and Systems Approach 15, no. 1 (2022): 1–18. http://dx.doi.org/10.4018/ijitsa.290004.

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This paper develops a rainfall prediction technique, named GWO-based Linear Regression (GWLR) model, using the linear regression model and Grey Wolf Optimizer (GWO). The linear regression model is used to predict the value of a dependent variable from an independent variable on the basis of regression coefficient. The proposed GWLR predicts rainfall based on the input time-series weather data using the proposed GWLR model, in which the regression coefficients are obtained optimally using the GWO. Thus, the rainfall detection is done on the accumulated data of India and the state, Jammu and Kas

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Deif, Mohanad A., Hani Attar, Ayman Amer, Haitham Issa, Mohammad R. Khosravi, and Ahmed A. A. Solyman. "A New Feature Selection Method Based on Hybrid Approach for Colorectal Cancer Histology Classification." Wireless Communications and Mobile Computing 2022 (May 5, 2022): 1–14. http://dx.doi.org/10.1155/2022/7614264.

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Colorectal cancer (CRC) is one of the most common malignant cancers worldwide. To reduce cancer mortality, early diagnosis and treatment are essential in leading to a greater improvement and survival length of patients. In this paper, a hybrid feature selection technique (RF-GWO) based on random forest (RF) algorithm and gray wolf optimization (GWO) was proposed for handling high dimensional and redundant datasets for early diagnosis of colorectal cancer (CRC). Feature selection aims to properly select the minimal most relevant subset of features out of a vast amount of complex noisy data to r

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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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Saswati, Sahoo, and Bala Behera Suman. "Image Segmentation using Different Optimization Technique." International Journal of Innovative Science and Research Technology 7, no. 7 (2022): 753–56. https://doi.org/10.5281/zenodo.6965411.

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In image segmentation field Multilevel thresholding is an important technique. However, in standard methods, the complexity of this method increases with the variation of number of thresholds value. To avoid this disadvantages, nature inspired meta-heuristic techniques are used. These metaheuristic algorithms give near exact results in a reasonable time, which catches the attention of recent researchers for optimization. No matter what kind of optimization method , the solution set must be represented via some way. For example, GWO (Grey Wolf Optimizer) this method follows the grouping and hun

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Mehmood, K., and A. Ahmad. "Improved Grey Wolf Optimization for Economic Load Dispatch Problem Considering Valve Point Loading Effect and Prohibited Operating Zones." Nucleus 54, no. 4 (2018): 250–57. https://doi.org/10.71330/thenucleus.2017.286.

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Economic load dispatch (ELD) is an important power system operational planning problem. In the past, calculus based techniques have been used for solving convex ELD problem. The practical ELD problem is non convex due to valve point effect. This paper presents a new improved grey wolf optimization (IGWO) for solving ELD problem considering constraints such as valve point effect, transmission losses and prohibited operating zones. Grey wolf optimization (GWO) is a swarm intelligence (SI) technique which suffers from stagnation. To overcome this problem differential mutation and crossover operat

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Ardiansyah, Sri Handayaningsih, and Deva Fathurrizki. "Grey Wolf Optimizer Termodifikasi Menggunakan Chaotic Uniform Initialization Untuk Estimasi Effort Cocomo." Jurnal Teknologi Informasi dan Ilmu Komputer 12, no. 3 (2025): 671–80. https://doi.org/10.25126/jtiik.2025128901.

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COCOMO merupakan metode estimasi effort perangkat lunak berbasis parametrik yang banyak digunakan dan fleksibel diimplementasikan pada organisasi skala kecil hingga besar. Akan tetapi, kedua parameter COCOMO, yaitu multiplikatif dan eksponensial kerap memberikan hasil yang kurang presisi serta tidak realistis untuk diterapkan pada lingkungan pengembangan perangkat lunak saat ini. Untuk mengatasi masalah tersebut, beberapa penelitian mengusulkan pendekatan berbasis pencarian untuk mendapatkan nilai parameter yang tepat dengan menggunakan algoritma optimasi metaheuristik. Grey Wolf Optimizer (GW

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Ardiansyah, Sri Handayaningsih, and Deva Fathurrizki. "Grey Wolf Optimizer Termodifikasi Menggunakan Chaotic Uniform Initialization Untuk Estimasi Effort Cocomo." Jurnal Teknologi Informasi dan Ilmu Komputer 12, no. 3 (2025): 671–80. https://doi.org/10.25126/jtiik.20258901.

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COCOMO merupakan metode estimasi effort perangkat lunak berbasis parametrik yang banyak digunakan dan fleksibel diimplementasikan pada organisasi skala kecil hingga besar. Akan tetapi, kedua parameter COCOMO, yaitu multiplikatif dan eksponensial kerap memberikan hasil yang kurang presisi serta tidak realistis untuk diterapkan pada lingkungan pengembangan perangkat lunak saat ini. Untuk mengatasi masalah tersebut, beberapa penelitian mengusulkan pendekatan berbasis pencarian untuk mendapatkan nilai parameter yang tepat dengan menggunakan algoritma optimasi metaheuristik. Grey Wolf Optimizer (GW

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Negi, Ganga, Anuj Kumar, Sangeeta Pant, and Mangey Ram. "Optimization of Complex System Reliability using Hybrid Grey Wolf Optimizer." Decision Making: Applications in Management and Engineering 4, no. 2 (2021): 241–56. http://dx.doi.org/10.31181/dmame210402241n.

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Reliability allocation to increase the total reliability has become a successful way to increase the efficiency of the complex industrial system designs. A lot of research in the past have tackled this problem to a great extent. This is evident from the different techniques developed so far to achieve the target. Stochastic metaheuristics like simulated annealing, Tabu search (TS), Particle Swarm Optimization (PSO), Cuckoo Search Optimization (CS), Genetic Algorithm (GA), Grey wolf optimization technique (GWO) etc. have been used in recent years. This paper proposes a framework for implementin

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Mohammadi, Babak, Yiqing Guan, Pouya Aghelpour, Samad Emamgholizadeh, Ramiro Pillco Zolá, and Danrong Zhang. "Simulation of Titicaca Lake Water Level Fluctuations Using Hybrid Machine Learning Technique Integrated with Grey Wolf Optimizer Algorithm." Water 12, no. 11 (2020): 3015. http://dx.doi.org/10.3390/w12113015.

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Lakes have an important role in storing water for drinking, producing hydroelectric power, and environmental, agricultural, and industrial uses. In order to optimize the use of lakes, precise prediction of the lake water level (LWL) is a main issue in water resources management. Due to the existence of nonlinear relations, uncertainty, and characteristics of the time series variables, the exact prediction of the lake water level is difficult. In this study the hybrid support vector regression (SVR) and the grey wolf algorithm (GWO) are used to predict lake water level fluctuations. Also, three

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Yetayew, Tefera, and Daniel Tesfaye. "A Comparative Analysis of Metaheuristic Algorithms Tuned Supper Twisting Sliding Mode Control of a Self-balancing Segway." Jordan Journal of Electrical Engineering 10, no. 4 (2024): 1. http://dx.doi.org/10.5455/jjee.204-1706551479.

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A self-balancing two-wheel Segway is a multivariable, nonlinear, coupled and unstable personal transport that is among the benchmarks of under-actuated systems to test different control schemes. The proper operation of the system needs stable and robust controllers for the balancing and direction of the Segway. Stable and robust performance of such systems can be achieved using adaptive, optimal and non-linear control schemes. A conventional sliding mode controller is a nonlinear controller, considered a robust controller except for the chattering problem that can be solved using higher order

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Saravanan, R., S. Subramanian, S. SooriyaPrabha, and S. Ganesan. "Generation scheduling with large-scale integration of renewable energy sources using grey wolf optimization." International Journal of Energy Sector Management 12, no. 4 (2018): 675–95. http://dx.doi.org/10.1108/ijesm-07-2016-0001.

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Purpose Generation scheduling (GS) is the most prominent and hard-hitting problem in the electrical power industry especially in an integrated power system. Countless techniques have been used so far to solve this GS problem for proper functioning of the units in the power system to dispatch the load economically to consumers at once. Therefore, this work aims to study for the best possible function of integrated power plants to obtain the most favourable solution to the GS problem. Design/methodology/approach An appropriate method works in a proper way and assures to give the best solution to

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Fahmi, Ahyar Izzaqi, Ayub Windarko Novie, and Asrarul Qudsi Ony. "Minimization of total harmonic distortion in neutral point clamped multilevel inverter using grey wolf optimizer." International Journal of Power Electronics and Drive Systems (IJPEDS) 13, no. 3 (2022): 1486–97. https://doi.org/10.11591/ijpeds.v13.i3.pp1486-1497.

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The inverter has been attracting researchers for their application in renewable energy. So far, multilevel inverter is considered as low distortion class, which produces multilevel output voltage imitating a pure sine waveform. However, the needs for free distortion of output voltage have been motivating to improve multilevel pulse width modulation PWM generation method. In this paper, the modified PWM technique is proposed to reduce the voltage total harmonics distortion (THD) of multilevel inverter. This modulation technique is then applied to control a single-phase three-level neutral point

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Bouaddi, Abdessamade, Reda Rabeh, and Mohammed Ferfra. "A fuzzy-PID controller for load frequency control of a two-area power system using a hybrid algorithm." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 4 (2024): 3580. http://dx.doi.org/10.11591/ijece.v14i4.pp3580-3591.

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This paper presents the use of a new hybrid optimization approach known as particle swarm optimization and grey wolf optimizer (PSO-GWO) for improving frequency stability load frequency control (LFC) in tow-area power systems. The approach consists in optimizing the fuzzy proportional-integral-derivative (fuzzy-PID) controller parameters with meta-heuristic hybrid algorithm: PSO-GWO. This technique allows to have dynamic responses with the least possible frequency deviation in very short response times. The approach proposes to controls the tie-line power and the frequency deviation in the con

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Shabeerkhan, S., and A. Padma. "A novel GWO optimized pruning technique for inexact circuit design." Microprocessors and Microsystems 73 (March 2020): 102975. http://dx.doi.org/10.1016/j.micpro.2019.102975.

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Ouhame, Soukaina, Youssef Hadi, and Arifullah Arifullah. "A Hybrid Grey Wolf Optimizer and Artificial Bee Colony Algorithm Used for Improvement in Resource Allocation System for Cloud Technology." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 14 (2020): 4. http://dx.doi.org/10.3991/ijoe.v16i14.16623.

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<p class="0abstract">Cloud computing is the next generation of technology which provide different service with the rule of pay and gain with the help of internet. These services consist of hardware and software used in different field of life. Due the growth of user in cloud environment the number of access and share system of technology increases which causes different issue and resource allocation system is one of them. In this paper for improvement in resource allocation system in VM a hybrid algorithm used because in some situation VM become underloaded and overloaded in cloud data c

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Dhakhinamoorthy, Chitradevi, Sathish Kumar Mani, Sandeep Kumar Mathivanan, et al. "Hybrid Whale and Gray Wolf Deep Learning Optimization Algorithm for Prediction of Alzheimer’s Disease." Mathematics 11, no. 5 (2023): 1136. http://dx.doi.org/10.3390/math11051136.

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In recent years, finding the optimal solution for image segmentation has become more important in many applications. The whale optimization algorithm (WOA) is a metaheuristic optimization technique that has the advantage of achieving the global optimal solution while also being simple to implement and solving many real-time problems. If the complexity of the problem increases, the WOA may stick to local optima rather than global optima. This could be an issue in obtaining a better optimal solution. For this reason, this paper recommends a hybrid algorithm that is based on a mixture of the WOA

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Ouhame, Soukaina, and Youssef Hadi. "Enhancement in resource allocation system for cloud environment using modified grey wolf technique." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 3 (2020): 1530–37. https://doi.org/10.11591/ijeecs.v20.i3.pp1530-1537.

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Cloud computing is new trend of technology which provides services with the help of internet based on specific rules.VM is one of the main elements of cloud computing it work on virtualizations concept. Due to the growth of cloud computing user demands for better service are increasing and it make different kind of issues in cloud environment. Data allocation sysytem in VM is one of them for that reason in this paper a new technique used for improvment of data allocation system in VM for cloud computing. The improvement took place GWO algorithm two main section of this algorithm are modified w

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Sharma, Abhishek, Abhinav Sharma, Averbukh Moshe, Nikhil Raj, and Rupendra Kumar Pachauri. "An Effective Method for Parameter Estimation of Solar PV Cell Using Grey-Wolf Optimization Technique." International Journal of Mathematical, Engineering and Management Sciences 6, no. 3 (2021): 911–31. http://dx.doi.org/10.33889/ijmems.2021.6.3.054.

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In the field of renewable energy, the extraction of parameters for solar photovoltaic (PV) cells is a widely studied area of research. Parameter extraction of solar PV cell is a highly non-linear complex optimization problem. In this research work, the authors have explored grey wolf optimization (GWO) algorithm to estimate the optimized value of the unknown parameters of a PV cell. The simulation results have been compared with five different pre-existing optimization algorithms: gravitational search algorithm (GSA), a hybrid of particle swarm optimization and gravitational search algorithm (

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Kumar, D. Mahesh, Dr S. Suresh Reddy, and Dr P. Sujatha. "Optimal DG and Capacitor Allocation Along with Network Reconfiguration Using Grey Wolf Optimizer Algorithm." International Journal of Electrical and Electronics Research 12, no. 4 (2024): 1495–501. https://doi.org/10.37391/ijeer.120445.

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This study introduces a multi-criteria optimization approach using the Grey Wolf Optimizer (GWO) algorithm to determine the optimal capacity, locations of DG units and capacitor banks, and network reconfiguration in distribution systems. The objective function incorporates six key performance metrics: real losses, imaginary losses, voltage variation, voltage stability index, section load ability, and current balancing index. The proposed GWO technique was evaluated on IEEE 33- and 69-bus systems and benchmarked against Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Results demon

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S., Mounika, R. Narmatha Banu, and B. Kiruthiga. "Optimal Placement Identification of Multiple DG Types Using Optimization Technique." E3S Web of Conferences 387 (2023): 01009. http://dx.doi.org/10.1051/e3sconf/202338701009.

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In this paper, a combination algorithm called GAIPSO, which combines GA and a better version of the classic particle swarm optimization process, is used. In order to calculate the data enhancement in voltage profile, this study uses the GWO algorithm. The ideal position for the proposed charging points inside the distribution system is the goal. The received comment thread solution (site & station size) is further re-optimized by PSO, improving both the functionality and outcome overall. Studies based on simulations show that the above mentioned technique outperforms GA, GWO, and PSO in re

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Ghada, Adel Aziz, Waley Shneen Salam, Nabeel Abdullah Fatin, and Harith Shaker Dina. "Advanced optimal GWO-PID controller for DC motor." International Journal of Advances in Applied Sciences (IJAAS) 11, no. 3 (2022): 263–76. https://doi.org/10.11591/ijaas.v11.i3.pp263-276.

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The current work aims to use traditional control algorithms and advanced optimization algorithms that was chosen for its ease of control and the possibility of using it in many industrial applications. By setting the appropriate specifications for the simulation model and after conducting the planned tests that simulate different applications of the motor’s work within electrical systems, the results proved to obtain good performance of the motor’s work, better response, high accuracy, in addition to the speed. The goal is to design and tune a proportional–integral–deri

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Bouaddi, Abdessamade, Reda Rabeh, and Mohammed Ferfra. "A fuzzy-PID controller for load frequency control of a two-area power system using a hybrid algorithm." A fuzzy-PID controller for load frequency control of a two-area power system using a hybrid algorithm 14, no. 4 (2024): 3580–91. https://doi.org/10.11591/ijece.v14i4.pp3580-3591.

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This paper presents the use of a new hybrid optimization approach known as particle swarm optimization and grey wolf optimizer (PSO-GWO) for improving frequency stability load frequency control (LFC) in tow-area power systems. The approach consists in optimizing the fuzzy proportional-integral-derivative (fuzzy-PID) controller parameters with meta-heuristic hybrid algorithm: PSO-GWO. This technique allows to have dynamic responses with the least possible frequency deviation in very short response times. The approach proposes to controls the tie-line power and the&

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Izzaqi, Fahmi Ahyar, Novie Ayub Windarko, and Ony Asrarul Qudsi. "Minimization of total harmonic distortion in neutral point clamped multilevel inverter using grey wolf optimizer." International Journal of Power Electronics and Drive Systems (IJPEDS) 13, no. 3 (2022): 1486. http://dx.doi.org/10.11591/ijpeds.v13.i3.pp1486-1497.

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<span lang="EN-US">The inverter has been attracting researchers for their application in renewable energy. So far, multilevel inverter is considered as low distortion class, which produces multilevel output voltage imitating a pure sine waveform. However, the needs for free distortion of output voltage have been motivating to improve multilevel pulse width modulation PWM generation method. In this paper, the modified PWM technique is proposed to reduce the voltage total harmonics distortion (THD) of multilevel inverter. This modulation technique is then applied to control a single-phase

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Preeti, Sharma, and C. Dhubkaraya D. "Cross-Layer Energy-Efficient Multimedia Transmission using Hybrid Fuzzy GWO in MIMO-OFDM." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 2137–40. https://doi.org/10.35940/ijeat.D7917.049420.

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Wireless communication multimedia applications are increasing day by day in traffic, security, agriculture and health care service sectors. Multimedia communication itself involves a complex process that consumes more power at transmitter and receiver. Our research evolves start to end layer design margin to reduce power consumption and increase multimedia data quality. In this we propose the MIMO-OFDM system with a hybrid fuzzy GWO technique to minimize consumed power. Cross-layer margins are optimized by Grey Wolf Optimizer with a fuzzy algorithm that analysed compression and communication t

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Ouhame, Soukaina, and Youssef Hadi. "Enhancement in resource allocation system for cloud environment using modified grey wolf technique." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 3 (2020): 1530. http://dx.doi.org/10.11591/ijeecs.v20.i3.pp1530-1537.

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<p class="normal">Cloud computing is an advanced technology which provides services with the help of internet. These services work under the rule of pay and gain. The services consist of hardware and software used in different fields of life. Due to growth of cloud computing the number of users are increased and their demand for better services also increased with the passage of time. Cloud computing faces different issues. One of them is resource scheduling. In this paper a new technique is used for improvement of scheduling in cloud computing. The improvement took place in GWO algorith

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Ghith, Ehab Saif, and Farid Abdel Aziz Tolba. "Real-time implementation of an enhanced proportional-integral-derivative controller based on sparrow search algorithm for micro-robotics system." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 4 (2022): 1395. http://dx.doi.org/10.11591/ijai.v11.i4.pp1395-1404.

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This paper presents a new approach to control the position of the microrobotics system with a proportional-integral-derivative (PID) controller. By using sparrow search algorithm (SSA), the optimal PID controller indicators were obtained by applying a new objective function namely, integral square time multiplied square error (ISTES). The effeciency of the proposed SSAbased controller was verified by comparisons made with grey wolf optimization (GWO) algorithm-based controllers in terms of time. Each control technique will be applied to the identified model using MATLAB Simulink and the experi

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R, Ramamoorthi, and Balamurugan R. "Solving Economic Load Dispatch Problem Using Grey Wolf Optimization Algorithm." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 2556–62. http://dx.doi.org/10.22214/ijraset.2023.52161.

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Abstract: This article presents a new evolutionary optimization approach named grey wolf optimization (GWO), which is based on the behavior of grey wolves, for the optimal operating strategy of economic load dispatch (ELD). Nonlinear characteristics of generators like ramp rate limits, valve point discontinuities and prohibited operating zones are considered in the problem. GWO method does not require any information about the gradient of the objective function, while searching for an optimum solution. The GWO algorithm concept appears to be a robust and reliable optimization algorithm is appl

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K. Faraj, Blqees, and Nazar K. ء. Hussein. "Gray Wolf Optimization and Least Square Estimatation As A New Learning Algorithm For Interval Type-II ANFIS." Tikrit Journal of Pure Science 24, no. 1 (2019): 107. http://dx.doi.org/10.25130/j.v24i1.832.

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Gray Wolfe Optimization (GWO) is one of the meta-heuristic method and it is a popular technique in Many engineering and economic applications. GWO and Least Square Estimatation (LSE) are used to optimize the antecedents and consequents parameters of interval type-2 ANFIS respectively. We are checking the new learning algorithm by using the interval type-2 ANFIS in prediction of Mackey-Glass time series and the results were very encouraging compared to other algorithms.

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Blqees K. Faraj and Nazar K. Hussein. "Gray Wolf Optimization and Least Square Estimatation As A New Learning Algorithm For Interval Type-II ANFIS." Tikrit Journal of Pure Science 24, no. 1 (2019): 107–11. http://dx.doi.org/10.25130/tjps.v24i1.339.

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Gray Wolfe Optimization (GWO) is one of the meta-heuristic method and it is a popular technique in Many engineering and economic applications. GWO and Least Square Estimatation (LSE) are used to optimize the antecedents and consequents parameters of interval type-2 ANFIS respectively. We are checking the new learning algorithm by using the interval type-2 ANFIS in prediction of Mackey-Glass time series and the results were very encouraging compared to other algorithms.

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Aziz, Ghada Adel, Salam Waley Shneen, Fatin Nabeel Abdullah, and Dina Harith Shaker. "Advanced optimal GWO-PID controller for DC motor." International Journal of Advances in Applied Sciences 11, no. 3 (2022): 263. http://dx.doi.org/10.11591/ijaas.v11.i3.pp263-276.

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<p><span>The current work aims to use traditional control algorithms and advanced optimization algorithms that was chosen for its ease of control and the possibility of using it in many industrial applications. By setting the appropriate specifications for the simulation model and after conducting the planned tests that simulate different applications of the motor’s work within electrical systems, the results proved to obtain good performance of the motor’s work, better response, high accuracy, in addition to the speed. The goal is to design and tune a <a name="_Hlk112847502"&gt

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Nagarajan, Vengatajalapathi, Ayyappan Solaiyappan, Siva Kumar Mahalingam, et al. "Meta-Heuristic Technique-Based Parametric Optimization for Electrochemical Machining of Monel 400 Alloys to Investigate the Material Removal Rate and the Sludge." Applied Sciences 12, no. 6 (2022): 2793. http://dx.doi.org/10.3390/app12062793.

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Electrochemical machining (ECM) is a preferred advanced machining process for machining Monel 400 alloys. During the machining, the toxic nickel hydroxides in the sludge are formed. Therefore, it becomes necessary to determine the optimum ECM process parameters that minimize the nickel presence (NP) emission in the sludge while maximizing the material removal rate (MRR). In this investigation, the predominant ECM process parameters, such as the applied voltage, flow rate, and electrolyte concentration, were controlled to study their effect on the performance measures (i.e., MRR and NP). A meta

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V.S.Acharyulu, B., P. K.Hota, and Banaja Mohanty. "Automatic Generation Control of Multi-Area Solar-Thermal Power System Using Fruit-Fly Optimization Algorithm." International Journal of Engineering & Technology 7, no. 4.5 (2018): 56. http://dx.doi.org/10.14419/ijet.v7i4.5.20009.

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In this paper, fruit-fly optimization algorithm (FOA) is applied to automatic generation control (AGC) of multi-area power systems. In the proposed three-area system, reheat thermal systems are considered in all areas incorporating solar thermal power plant (STPP) in one of the areas. The optimum gain of proportional-integral-derivative (PID) controller is optimized applying FOA technique. The strength of FOA is established by comparing the results with well-established Grey Wolf optimizer (GWO) technique for the same interconnected power system. The performances of the system with FOA techniq

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Tinnathi, Sreenivasu, and G. Sudhavani. "Copy-Move Forgery Detection Using Superpixel Clustering Algorithm and Enhanced GWO Based AlexNet Model." Cybernetics and Information Technologies 22, no. 4 (2022): 91–110. http://dx.doi.org/10.2478/cait-2022-0041.

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Abstract In this work a model is introduced to improve forgery detection on the basis of superpixel clustering algorithm and enhanced Grey Wolf Optimizer (GWO) based AlexNet. After collecting the images from MICC-F600, MICC-F2000 and GRIP datasets, patch segmentation is accomplished using a superpixel clustering algorithm. Then, feature extraction is performed on the segmented images to extract deep learning features using an enhanced GWO based AlexNet model for better forgery detection. In the enhanced GWO technique, multi-objective functions are used for selecting the optimal hyper-parameter

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