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Journal articles on the topic 'GWO and PSO'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

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

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

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

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

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

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

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Qasim, Kian Raheem, Noor M. Naser, and Ahmed J. Jabur. "An IoT-Enhanced Traffic Light Control System with Arduino and IR Sensors for Optimized Traffic Patterns." Future Internet 16, no. 10 (2024): 377. http://dx.doi.org/10.3390/fi16100377.

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Traffic lights play an important role in efficient traffic management, especially in crowded cities. Optimizing traffic helps to reduce crowding, save time, and ensure the smooth flow of traffic. Metaheuristic algorithms have a proven ability to optimize smart traffic management systems. This paper investigates the effectiveness of two metaheuristic algorithms: particle swarm optimization (PSO) and grey wolf optimization (GWO). In addition, we posit a hybrid PSO-GWO method of optimizing traffic light control using IoT-enabled data from sensors. In this study, we aimed to enhance the movement o

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Dang, Minh Phung, Hieu Giang Le, Ngoc Phat Nguyen, Ngoc Le Chau, and Thanh-Phong Dao. "Optimization for a New XY Positioning Mechanism by Artificial Neural Network-Based Metaheuristic Algorithms." Computational Intelligence and Neuroscience 2022 (December 1, 2022): 1–18. http://dx.doi.org/10.1155/2022/9151146.

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This paper devotes a new method in modeling and optimizing to handle the optimization of the XY positioning mechanism. The fitness functions and constraints of the mechanism are formulated via proposing a combination of artificial neural network (ANN) and particle swarm optimization (PSO) methods. Next, the PSO is hybridized with the grey wolf optimization, namely PSO-GWO, which is applied to three scenarios in handling the single objective function. In order to search the multiple functions for the mechanism, the multiobjective optimization genetic algorithm (MOGA) is applied to the last scen

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

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

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

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

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Hassan S. Ahmed, Ahmed J. Abid, Adel A. Obed, Ameer L. Saleh, and Reheel J. Hassoon. "Maximizing Energy Output of Photovoltaic Systems: Hybrid PSO-GWO-CS Optimization Approach." Journal of Techniques 5, no. 3 (2023): 174–84. http://dx.doi.org/10.51173/jt.v5i3.1312.

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Photovoltaic (PV) systems suffer from partial shade and nonuniform irradiance conditions. Meanwhile, each PV module has a bypass shunt diode (BSD) to prevent hotspots. BSD also causes a series of a peak in the power-voltage characteristics of the PV array, trapping traditional maximum Power Point Tracking (MPPT) methods in local peaks. This study aims to address these challenges by combining cuckoo search (CS), gray wolf optimization (GWO), and particle swarm optimization (PSO) to enhance MPPT performance. The results compared the yield power by Tracking the MPP using only GWO, CS, or PSO MPPT

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9

Camas-Náfate, Mónica, Alberto Coronado-Mendoza, Carlos Vargas-Salgado, Jesús Águila-León, and David Alfonso-Solar. "Optimizing Lithium-Ion Battery Modeling: A Comparative Analysis of PSO and GWO Algorithms." Energies 17, no. 4 (2024): 822. http://dx.doi.org/10.3390/en17040822.

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In recent years, the modeling and simulation of lithium-ion batteries have garnered attention due to the rising demand for reliable energy storage. Accurate charge cycle predictions are fundamental for optimizing battery performance and lifespan. This study compares particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithms in modeling a commercial lithium-ion battery, emphasizing the voltage behavior and the current delivered to the battery. Bio-inspired optimization tunes parameters to reduce the root mean square error (RMSE) between simulated and experimental outputs. The

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10

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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11

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

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

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12

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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Khaseeb, Jomana Yousef, Arabi Keshk, and Anas Youssef. "Improved Binary Grey Wolf Optimization Approaches for Feature Selection Optimization." Applied Sciences 15, no. 2 (2025): 489. https://doi.org/10.3390/app15020489.

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Feature selection is a preprocessing step for various classification tasks. Its objective is to identify the most optimal features in a dataset by eliminating redundant data while preserving the highest possible classification accuracy. Three improved binary Grey Wolf Optimization (GWO) approaches are proposed in this paper to optimize the feature selection process by enhancing the feature selection accuracy while selecting the least possible number of features. Each approach combines GWO with Particle Swarm Optimization (PSO) by implementing GWO followed by PSO. Afterwards, each approach mani

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14

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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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.

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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

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Vinay H. Keswania. "Design of an Integrated Method for Optimizing Electron Beam Lithography Using Genetic Algorithms, Particle Swarm Optimization, and Grey Wolf Optimizer." Advances in Nonlinear Variational Inequalities 27, no. 4 (2024): 434–56. http://dx.doi.org/10.52783/anvi.v27.1608.

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With the ever-increasing demands of advanced manufacturing for precise micro-and nanoscale patterning, optimization of the EBL process is in urgent need. The current optimization methods involve combinations such as GA with GWO or PSO with GWO, which are plagued by poor exploration-exploitation trade-offs and hence converge to suboptimal solutions or insufficient refinement of the solution. The above challenges are overcome by an innovative Adaptive Wolf-Driven Swarm Evolution approach, synergizing the strengths of GA, PSO, and GWO for the optimization process of EBL. The generation of a diver

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Kamaruddin, Ami Shamril, Mohd Fikri Hadrawi, Yap Bee Wah, and Sharifah Aliman. "An evaluation of nature-inspired optimization algorithms and machine learning classifiers for electricity fraud prediction." Indonesian Journal of Electrical Engineering and Computer Science 32, no. 1 (2023): 468. http://dx.doi.org/10.11591/ijeecs.v32.i1.pp468-477.

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<span>This study evaluated the nature-inspired optimization algorithms to improve classification involving imbalanced class problems. The particle swarm optimization (PSO) and grey wolf optimizer (GWO) were used to adaptively balance the distribution and then four supervised machine learning classifiers artificial neural network (ANN), support vector machine (SVM), extreme gradient-boosted tree (XGBoost), and random forest (RF) were applied to maximize the classification performance for electricity fraud prediction. The imbalance data was balanced using random undersampling (RUS) and two

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Ami, Shamril Kamaruddin, Fikri Hadrawi Mohd, Bee Wah Yap, and Aliman Sharifah. "An evaluation of nature-inspired optimization algorithms and machine learning classifiers for electricity fraud prediction." An evaluation of nature-inspired optimization algorithms and machine learning classifiers for electricity fraud prediction 32, no. 1 (2023): 468–77. https://doi.org/10.11591/ijeecs.v32.i1.pp468-477.

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This study evaluated the nature-inspired optimization algorithms to improve classification involving imbalanced class problems. The particle swarm optimization (PSO) and grey wolf optimizer (GWO) were used to adaptively balance the distribution and then four supervised machine learning classifiers artificial neural network (ANN), support vector machine (SVM), extreme gradient-boosted tree (XGBoost), and random forest (RF) were applied to maximize the classification performance for electricity fraud prediction. The imbalance data was balanced using random undersampling (RUS) and two nature-insp

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19

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

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

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Mwakitalima, Isaka J., Mohammad Rizwan, and Narendra Kumar. "Potential of a Nonperennial Tributary Integrated with Solar Energy for Rural Electrification: A Case Study of Ikukwa Village in Tanzania." Mathematical Problems in Engineering 2022 (May 16, 2022): 1–37. http://dx.doi.org/10.1155/2022/1172050.

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This study evaluates the hydropower potential in the design of a micro-hydro/solar photovoltaic hybrid system with battery energy storage for increasing the access to electricity in Ikukwa Village in Mbeya Region of Tanzania. Usually, hybridized hydropower schemes are designed from perennial streams for the provision of electricity. This study incorporates the run-of-the river (COE) power scheme, which originates from the untapped potential of nonperennial hydro-energy source and the use of traditional approach of data measurements for Ikata tributary to design hybrid system. The system is opt

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Sule, Aliyu Hamza, Ahmad Safawi Mokhtar, Jasrul Jamani Bin Jamian, Attaullah Khidrani, and Raja Masood Larik. "Optimal tuning of proportional integral controller for fixed-speed wind turbine using grey wolf optimizer." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (2020): 5251. http://dx.doi.org/10.11591/ijece.v10i5.pp5251-5261.

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The need for tuning the PI controller is to improve its performance metrics such as rise time, settling time and overshoot. This paper proposed the Grey Wolf Optimizer (GWO) tuning method of a Proportional Integral (PI) controller for fixed speed Wind Turbine. The objective is to overcome the limitations in using the PSO and GA tuning methods for tuning the PI controller, such as quick convergence occurring too soon into a local optimum, and the controller step input response. The GWO, the Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA) tuning methods were implemented in the

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Aliyu, Hamza Sule, Safawi Mokhtar Ahmad, Jamani Bin Jamian Jasrul, Khidrani Attaullah, and Masood Larik Raja. "Optimal tuning of proportional integral controller for fixed-speed wind turbine using grey wolf optimizer." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (2020): 5251–61. https://doi.org/10.11591/ijece.v10i5.pp5251-5261.

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The need for tuning the PI controller is to improve its performance metrics such as rise time, settling time and overshoot. This paper proposed the Grey Wolf Optimizer (GWO) tuning method of a Proportional Integral (PI) controller for fixed speed Wind Turbine. The objective is to overcome the limitations in using the Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) tuning methods for tuning the PI controller, such as quick convergence occurring too soon into a local optimum, and overshoot of the controller step input response. The GWO, the PSO, and the GA tuning methods were implem

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23

Gursoy Demir, Habibe. "Grey Wolf Optimization- and Particle Swarm Optimization-Based PD/I Controllers and DC/DC Buck Converters Designed for PEM Fuel Cell-Powered Quadrotor." Drones 9, no. 5 (2025): 330. https://doi.org/10.3390/drones9050330.

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The most important criterion in the design of unmanned air vehicles is to successfully complete the given task and consume minimum energy in the meantime. This paper presents a comparison of the performances of metaheuristic methods such as Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) to design controllers and DC/DC buck converters for optimizing the energy consumption and path following error of a PEM fuel cell-powered quadrotor system. Hence, the system consists of two PSO- and GWO-based optimizers. Optimizer I is used for determining the parameters of the PD controller

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

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

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Gupta, Priyank, Sanjay Kumar Gupta, and Rakesh Singh Jadon. "AGGREGATION AND HYBRID APPROACHES USING BPSO AND BGWO FOR FEATURE SELECTION FOR INDIAN STOCK INDEX PRICE PREDICTION USING GRU." International Journal of Engineering Applied Sciences and Technology 8, no. 3 (2023): 94–104. http://dx.doi.org/10.33564/ijeast.2023.v08i03.013.

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The stock exchange is a significant component of the economy, and forecasting its development is essential. Several deep-learning time series models based on RNN and its variants are used to forecast the stock market, but their accuracy still needs to be improved. Optimization strategies, such as GA, PSO, GWO, and others, have been used to enhance the reliability of these models. In this proposed paper, we have used the Binary GWO and Binary PSO algorithms to optimize the input characteristics of a two-layer GRU model for forecasting Indian stock index price. To increase the accuracy of these

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Hou, Yunfei, Yingnan Zhang, Wenzhu Gui, Di Wang, and Wei Dong. "Meshless Search SR-STAP for Airborne Radar Based on Meta-Heuristic Algorithms." Sensors 23, no. 23 (2023): 9444. http://dx.doi.org/10.3390/s23239444.

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The sparse recovery (SR) space-time adaptive processing (STAP) method has excellent clutter suppression performance under the condition of limited observation samples. However, when the cluttering is nonlinear in a spatial-Doppler profile, it will cause an off-grid effect and reduce the sparse recovery performance. A meshless search using a meta-heuristic algorithm (MH) can completely eliminate the off-grid effect in theory. Therefore, genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), and grey wolf optimization (GWO) methods are applied to SR-STAP for sele

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

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

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Ataşlar-Ayyıldız, Banu. "Robust Trajectory Tracking Control for Serial Robotic Manipulators Using Fractional Order-Based PTID Controller." Fractal and Fractional 7, no. 3 (2023): 250. http://dx.doi.org/10.3390/fractalfract7030250.

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The design of advanced robust control is crucial for serial robotic manipulators under various uncertainties and disturbances in case of the forceful performance needs of industrial robotic applications. Therefore, this work has proposed the design and implementation of a fractional order proportional tilt integral derivative (FOPTID) controller in joint space for a 3-DOF serial robotic manipulator. The proposed controller has been designed based on the fractional calculus concept to fulfill trajectory tracking with high accuracy and also reduce effects from disturbances and uncertainties. The

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Vargas, Maria, Domingo Cortes, Marco Antonio Ramirez-Salinas, Luis Alfonso Villa-Vargas, and Antonio Lopez. "Random Exploration and Attraction of the Best in Swarm Intelligence Algorithms." Applied Sciences 14, no. 23 (2024): 11116. http://dx.doi.org/10.3390/app142311116.

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In this paper, it is revealed that random exploration and attraction of the best (REAB) are two underlying procedures in many swarm intelligence algorithms. This is particularly shown in two of the most known swarm algorithms: the particle swarm optimization (PSO) and gray wolf optimizer (GWO) algorithms. From this observation, it is here proposed that instead of building algorithms based on a narrative derived from observing some animal behavior, it is more convenient to focus on algorithms that perform REAB procedures; that is, to build algorithms to make a wide and efficient explorations of

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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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32

Abedulabbas, Ghufran W., and Farazdaq R. Yaseen. "Design a PI Controller Based on PSO and GWO for a Brushless DC Motor." Journal Européen des Systèmes Automatisés 55, no. 3 (2022): 331–38. http://dx.doi.org/10.18280/jesa.550305.

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Recently all the moving mechanical parts that are subjected to wear and cause errors in the future are replaced with the equivalent of electrical. A Brushless Direct Current (BLDC) motor is preferable compared to a brushed DC motor because it substitutes the unit of mechanical commutations with an electronic unit, enhancing dynamic properties, noise level, and efficiency. Since it is fairly inexpensive, simple in structure, and performs well, maximum BLDC motor drives use a Proportional-Integral PI controller for controlling the machine's speed. The major issue with the PI controller, on the o

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Faulinda, Ely Nastiti, Musa Shahrulniza, and Yafi Eiad. "A hybrid deep learning optimization for predicting the spread of a new emerging infectious disease." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 2036–48. https://doi.org/10.11591/ijai.v13.i2.pp2036-2048.

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In this study, a novel approach geared toward predicting the estimated number of coronavirus disease (COVID-19) cases was developed. Combining long short-term memory (LSTM) neural networks with particle swarm optimization (PSO) along with grey wolf optimization (GWO) employ hybrid optimization algorithm techniques. This investigation utilizes COVID-19 original data from the Ministry of Health of Indonesia, period 2020-2021. The developed LSTM-PSO-GWO hybrid optimization algorithm can improve the performance and accuracy of predicting the spread of the COVID-19 virus in Indonesia. In initiating

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Almseidin, Mohammad, Amjad Gawanmeh, Maen Alzubi, Jamil Al-Sawwa, Ashraf S. Mashaleh, and Mouhammd Alkasassbeh. "Hybrid Deep Neural Network Optimization with Particle Swarm and Grey Wolf Algorithms for Sunburst Attack Detection." Computers 14, no. 3 (2025): 107. https://doi.org/10.3390/computers14030107.

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Deep Neural Networks (DNNs) have been widely used to solve complex problems in natural language processing, image classification, and autonomous systems. The strength of DNNs is derived from their ability to model complex functions and to improve detection engines through deeper architecture. Despite the strengths of DNN engines, they present several crucial challenges, such as the number of hidden layers, the learning rate, and the neuron weight. These parameters are considered to play a crucial role in the ability of DNNs to detect anomalies. Optimizing these parameters could improve the det

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Nastiti, Faulinda Ely, Shahrulniza Musa, and Eiad Yafi. "A hybrid deep learning optimization for predicting the spread of a new emerging infectious disease." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 2036. http://dx.doi.org/10.11591/ijai.v13.i2.pp2036-2048.

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<span lang="EN-US">In this study, a novel approach geared toward predicting the estimated number of coronavirus disease (COVID-19) cases was developed. Combining long short-term memory (LSTM) neural networks with particle swarm optimization (PSO) along with grey wolf optimization (GWO) employ hybrid optimization algorithm techniques. This investigation utilizes COVID-19 original data from the Ministry of Health of Indonesia, period 2020-2021. The developed LSTM-PSO-GWO hybrid optimization algorithm can improve the performance and accuracy of predicting the spread of the COVID-19 virus in

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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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Ayad, Abdulrahem Alabdalbari, and Ahmed Abed Issa. "New robot path planning optimization using hybrid GWO-PSO algorithm." Bulletin of Electrical Engineering and Informatics 11, no. 3 (2022): 1289~1296. https://doi.org/10.11591/eei.v11i3.3677.

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Actually, path planning is one of the most crucial aspects of mobile robots study. The primary goal of this research is to develop a solution to the path planning issues that occur when a “mobile robot” operates in a static environment. The problem is handled by finding a collision-free path that meets the given criteria for the shortest distance with quite the smoothness of the path. Two nature-inspired metaheuristic algorithms are used in the computation. By leading a hybrid “gray wolf optimization” with the “particle swarm optimization” (HGWO-PSO) computa

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Liu, Yizhe, Yu Jiang, Xin Zhang, Yong Pan, and Yingquan Qi. "Combined Grey Wolf Optimizer Algorithm and Corrected Gaussian Diffusion Model in Source Term Estimation." Processes 10, no. 7 (2022): 1238. http://dx.doi.org/10.3390/pr10071238.

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It is extremely critical for an emergency response to quickly and accurately use source term estimation (STE) in the event of hazardous gas leakage. To determine the appropriate algorithm, four swarm intelligence optimization (SIO) algorithms including Gray Wolf optimizer (GWO), particle swarm optimization (PSO), genetic algorithm (GA) and ant colony optimization (ACO) are selected to be applied in STE. After calculation, all four algorithms can obtain leak source parameters. Among them, GWO and GA have similar computational efficiency, while ACO is computationally inefficient. Compared with G

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Alabdalbari, Ayad Abdulrahem, and Issa Ahmed Abed. "New robot path planning optimization using hybrid GWO-PSO algorithm." Bulletin of Electrical Engineering and Informatics 11, no. 3 (2022): 1289–96. http://dx.doi.org/10.11591/eei.v11i3.3677.

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Actually, path planning is one of the most crucial aspects of mobile robots study. The primary goal of this research is to develop a solution to the path planning issues that occur when a “mobile robot” operates in a static environment. The problem is handled by finding a collision-free path that meets the given criteria for the shortest distance with quite the smoothness of the path. Two nature-inspired metaheuristic algorithms are used in the computation. By leading a hybrid “gray wolf optimization” with the “particle swarm optimization” (HGWO-PSO) computation that restricts the distance and

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Gurbani, Kaur, and Kumar Dharmender. "Classification of Intrusion using Artificial Neural Network with GWO." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 599–606. https://doi.org/10.5281/zenodo.5554561.

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In the present milieu of connected world, where security is the major concern, Intrusion Detection System is the prominent area of research to deal with various types of attacks in network. Intrusion detection systems (IDS) finds the dynamic and malicious traffic of network, in accordance to the aspect of network. Various form of IDS has been developed working on distinctive approaches. One popular approach is machine learning in which various algorithms like ANN, SVM etc. have been used. But the most prominent method used is ANN. The performance of the ANN can significantly be improved by com

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Parveen Kumar, Et al. "Investigation of Evolutionary Computation Techniques for Enhancing Solar Photovoltaic Cell Performance." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2024): 4742–48. http://dx.doi.org/10.17762/ijritcc.v11i9.10025.

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The pursuit of optimized solar photovoltaic (PV) cell parameters is critical for advancing renewable energy technologies amidst global energy security and climate change challenges. This research investigates the efficacy of particle swarm optimization (PSO) and gray wolf optimization (GWO) in fine-tuning PV cell behavior parameters. Leveraging evolutionary computation, the study aims to maximize energy output, minimize costs, and enhance system reliability by optimizing material properties, structural configurations, and operating conditions. Through iterative optimization, PSO and GWO naviga

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Hafiz, Minhajuddin Shaikh, R. Kulkarni Neelima, and V. Bakshi Mayuresh. "Computational analysis of hybrid grey wolf and particle swarm optimization for water level control in coupled tank." Computational analysis of hybrid grey wolf and particle swarm optimization for water level control in coupled tank 32, no. 2 (2023): 763–72. https://doi.org/10.11591/ijeecs.v32.i2.pp763-772.

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Water level control at a precise set point is a major concern in process control systems such as bulk drug production industries. Loss in production at the initial stage is observed until the water level reaches desired level. Pharmaceutical industries can however be benefited if they could maintain the precise water level control at the initial stage of production with the help of the performance optimization using various techniques. To achieve optimized value of the selected performance index, a hybrid of particle swarm optimization (PSO) and grey wolf optimization (GWO) is found to provide

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Altaie, Atica M., Tawfeeq Mokdad Tawfeeq, and Mustafa Ghanem Saeed. "Automated Test Suite Generation Tool based on GWO Algorithm." Webology 19, no. 1 (2022): 3835–49. http://dx.doi.org/10.14704/web/v19i1/web19252.

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In succession, the size and complexity of the program increase and the scope of testing expand. So, to ensure deadline delivery and reduce development testing costs, program testing efficiency must be improved. Therefore, to ensure that the product is delivered on the deadline and the cost of testing development is reduced, the efficiency of the program's testing must be enhanced. In this study, highlighting is placed to generate test suite automatically to reach increase the coverage of paths based on two algorithms Grey Wolf Optimizer algorithm (GWO) and Particle swarm optimization (PSO). Th

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Sriperambuduri, Vinay Kumar, and Nagaratna M. "A Hybrid Grey Wolf Optimization and Constriction Factor based PSO Algorithm for Workflow Scheduling in Cloud." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9s (2023): 718–26. http://dx.doi.org/10.17762/ijritcc.v11i9s.7744.

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Due to its flexibility, scalability, and cost-effectiveness of cloud computing, it has emerged as a popular platform for hosting various applications. However, optimizing workflow scheduling in the cloud is still a challenging problem because of the dynamic nature of cloud resources and the diversity of user requirements. In this context, Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) algorithms have been proposed as effective techniques for improving workflow scheduling in cloud environments. The primary objective of this work is to propose a workflow scheduling algorithm

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

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

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Xie, Fengxi, Guozhen Liang, and Ying-Ren Chien. "Highly Robust Adaptive Sliding Mode Trajectory Tracking Control of Autonomous Vehicles." Sensors 23, no. 7 (2023): 3454. http://dx.doi.org/10.3390/s23073454.

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Autonomous driving technology has not yet been widely adopted, in part due to the challenge of achieving high-accuracy trajectory tracking in complex and hazardous driving scenarios. To this end, we proposed an adaptive sliding mode controller optimized by an improved particle swarm optimization (PSO) algorithm. Based on the improved PSO, we also proposed an enhanced grey wolf optimization (GWO) algorithm to optimize the controller. Taking the expected trajectory and vehicle speed as inputs, the proposed control scheme calculates the tracking error based on an expanded vector field guidance la

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Riaz, Muhammad, Aamir Hanif, Shaik Javeed Hussain, Muhammad Irfan Memon, Muhammad Umair Ali, and Amad Zafar. "An Optimization-Based Strategy for Solving Optimal Power Flow Problems in a Power System Integrated with Stochastic Solar and Wind Power Energy." Applied Sciences 11, no. 15 (2021): 6883. http://dx.doi.org/10.3390/app11156883.

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In an effort to reduce greenhouse gas emissions, experts are looking to substitute fossil fuel energy with renewable energy for environmentally sustainable and emission free societies. This paper presents the hybridization of particle swarm optimization (PSO) with grey wolf optimization (GWO), namely a hybrid PSO-GWO algorithm for the solution of optimal power flow (OPF) problems integrated with stochastic solar photovoltaics (SPV) and wind turbines (WT) to enhance global search capabilities towards an optimal solution. A solution approach is used in which SPV and WT output powers are estimate

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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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Rajakumar, R., J. Amudhavel, P. Dhavachelvan, and T. Vengattaraman. "GWO-LPWSN: Grey Wolf Optimization Algorithm for Node Localization Problem in Wireless Sensor Networks." Journal of Computer Networks and Communications 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/7348141.

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Seyedali Mirjalili et al. (2014) introduced a completely unique metaheuristic technique particularly grey wolf optimization (GWO). This algorithm mimics the social behavior of grey wolves whereas it follows the leadership hierarchy and attacking strategy. The rising issue in wireless sensor network (WSN) is localization problem. The objective of this problem is to search out the geographical position of unknown nodes with the help of anchor nodes in WSN. In this work, GWO algorithm is incorporated to spot the correct position of unknown nodes, so as to handle the node localization problem. The

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Mahmood, Sozan Abdulla, and Qani Qabil Qasim. "Big Data Sentimental Analysis Using Document to Vector and Optimized Support Vector Machine." UHD Journal of Science and Technology 4, no. 1 (2020): 18. http://dx.doi.org/10.21928/uhdjst.v4n1y2020.pp18-28.

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With the rapid evolution of the internet, using social media networks such as Twitter, Facebook, and Tumblr, is becoming so common that they have made a great impact on every aspect of human life. Twitter is one of the most popular micro-blogging social media that allow people to share their emotions in short text about variety of topics such as company’s products, people, politics, and services. Analyzing sentiment could be possible as emotions and reviews on different topics are shared every second, which makes social media to become a useful source of information in different fields such as

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