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An effective model parameter estimation of PEMFCs using gwo algorithm and its variants

IET RENEWABLE POWER GENERATION 2022年第7期16卷 1380-1400页

作者： Diab, Ahmed A. Zaki Abdul-Ghaffar, Hussien, I Ahmed, Abdelsalam A. Ramadan, Husam A. Minia Univ Fac Engn Elect Engn Dept Al Minya 61111 Egypt Bani Swief Technol Univ Egyptian Korean Fac Ind & Energy Technol Mechatron Dept Bani Swief Egypt New Urban Communities Author New Minia City Minia Egypt Tanta Univ Fac Engn Elect Power & Machines Engn Dept Tanta Egypt

This paper introduces the application of the variants of the Grey Wolf Optimisation algorithm for the sake of assessing unknown parameters of Proton Exchange Membrane Fuel Cells models. Three versions of Grey Wolf Optimisation algorithm are applied: Conventional Grey Wolf Optimisation, Improved Grey Wolf Optimisation based on dimension learning-based hunting, and Selective Opposition-based Grey Wolf Optimisation. Moreover, Optimisation algorithms of Ant Lion Optimiser, Atom search optimisation, Dragonfly algorithm, and Multi-Verse Optimiser have been applied to estimate the parameters of Proton Exchange Membrane Fuel Cells models for the validation purpose. These algorithms are applied to three commercial Proton Exchange Membrane Fuel Cells stacks: BCS 500W-PEM, 500W-SR-12PEM and 250W-stack. The analyses are executed considering several operational circumstances. Sum of square errors value of the results based on parameters estimation and those experimentally tested are calculated. The objective function is chosen as sum of square errors value. The results are compared with those obtained using well-known methods in the literature to validate the effectiveness of the proposed methods. It is noticeable that the simulated I-V curves momentously match the datasheet curves for all the studied cases. In addition, considering the accuracy of the solution and the convergence speed, the Proton Exchange Membrane Fuel Cells model based on the Improved Grey Wolf Optimisation algorithm excels all other algorithms. Based on the simulation results, the Improved Grey Wolf Optimisation algorithm can improve the optimisation efficiency to 99.97 for the 250 W stack while the efficiency with Grey Wolf Optimisation and Selective Opposition-based Grey Wolf Optimisation are 98.83 and 98.84, respectively, for the 250 W stack case study.

关键词： BCS 500W-PEM proton exchange membrane fuel cells model 250W-stack dimension learning-based hunting PEMFC Dragonfly algorithm square errors value optimisation gwo algorithm atom search optimisation parameter estimation MultiVerse optimiser conventional grey wolf optimisation selective opposition-based grey wolf optimisation parameters estimation optimisation efficiency commercial Proton Exchange Membrane Fuel Cells stacks 500W-SR-12PEM Ant Lion optimiser proton exchange membrane fuel cells

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Automatic lip-reading classification using deep learning approaches and optimized quaternion meixner moments by gwo algorithm

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KNOWLEDGE-BASED SYSTEMS 2024年 304卷

作者： El Ogri, Omar EL-Mekkaoui, Jaouad Benslimane, Mohamed Hjouji, Amal Sidi Mohamed Ben Abdellah Univ Higher Sch Technol LSEM Fes Morocco Sidi Mohamed Ben Abdellah Fez Univ Dhar El Mahrez Fac Sci Lab Informat Signals Automat & Cognitivism LISAC CED STSTIC Fes Morocco

Lip-reading classification has received a lot of interest in recent decades because it is widely used in a variety of fields. It plays an important role in interpreting spoken words in noisy situations and reconstructing communication processes for those with hearing impairments. Despite significant advancements in this field, there are still several drawbacks in existing work such as feature extraction and Model capability for visual speech recognition. For these reasons, the current paper suggests an Optimized Quaternion Meixner Moments Convolutional Neural Network (OQMMs-CNN) method that intends to develop a Visual Speech Recognition (VSR) system based only on video images. This unique method combines OQMMs optimized for the gwo algorithm and convolutional neural networks taken from deep learning techniques with the aim of recognizing digits, words, or letters displayed as input *** OQMMs are used here as descriptors with the purpose of identifying, holding, and extracting essential information from video images (lips images) and generating moments for CNN input. The latter uses Meixner polynomials, which are defined by local parameters alpha and beta. Then, the Grey Wolf optimization method (gwo) is applied to enssure excellent classification accuracy by optimizing those local parameters. After being tested on three public datasets such as AVLetters, Grid, AVDigits, and LRW, and comparing to several ways using complicated models and deep architecture, the method emerges as an excellent solution for reducing the high dimensionality of video pictures and training time.

关键词： Convolutional neural networks Orthogonal polynomials Quaternion meixner moments gwo algorithm Lip-reading classification

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Path Planning for Unmanned Delivery Robots Based on EWB-gwo algorithm

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SENSORS 2023年第4期23卷 1867页

作者： Luo, Yuan Qin, Qiong Hu, Zhangfang Zhang, Yi Chongqing Univ Posts & Telecommun Key Lab Optoelect Informat Sensing & Technol Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Sch Adv Mfg Engn Chongqing 400065 Peoples R China

With the rise of robotics within various fields, there has been a significant development in the use of mobile robots. For mobile robots performing unmanned delivery tasks, autonomous robot navigation based on complex environments is particularly important. In this paper, an improved Gray Wolf Optimization (gwo)-based algorithm is proposed to realize the autonomous path planning of mobile robots in complex scenarios. First, the strategy for generating the initial wolf pack of the gwo algorithm is modified by introducing a two-dimensional Tent-Sine coupled chaotic mapping in this paper. This guarantees that the gwo algorithm generates the initial population diversity while improving the randomness between the two-dimensional state variables of the path nodes. Second, by introducing the opposition-based learning method based on the elite strategy, the adaptive nonlinear inertia weight strategy and random wandering law of the Butterfly Optimization algorithm (BOA), this paper improves the defects of slow convergence speed, low accuracy, and imbalance between global exploration and local mining functions of the gwo algorithm in dealing with high-dimensional complex problems. In this paper, the improved algorithm is named as an EWB-gwo algorithm, where EWB is the abbreviation of three strategies. Finally, this paper enhances the rationalization of the initial population generation of the EWB-gwo algorithm based on the visual-field line detection technique of Bresenham's line algorithm, reduces the number of iterations of the EWB-gwo algorithm, and decreases the time complexity of the algorithm in dealing with the path planning problem. The simulation results show that the EWB-gwo algorithm is very competitive among metaheuristics of the same type. It also achieves optimal path length measures and smoothness metrics in the path planning experiments.

关键词： mobile robot gwo algorithm path planning two-dimensional chaotic mapping adaptive nonlinear inertia weights BOA algorithm opposition-based learning

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Multi-Stage Cooperative Optimization Control for Photovoltaic MPPT: A High-Efficiency Gray Wolf Optimizer-Incremental Conductance Hybrid Strategy

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ENERGIES 2025年第8期18卷 1977-1977页

作者： Li, Jiahao Lu, Shuai Yang, Jing Henan Univ Sci & Technol Coll Informat Engn Luoyang 471000 Peoples R China Chengdu HuaMod Technol Co Ltd Chengdu 610000 Peoples R China

With the continuous growth of global energy demand and the increasing severity of environmental issues, photovoltaic (PV) power generation, as a clean and renewable energy source, has attracted widespread attention. However, the performance of PV systems is easily affected by factors such as irradiance and temperature in complex environments, leading to significant fluctuations in output power and making it difficult to achieve stable and efficient energy conversion. To address this issue, this paper proposes an enhanced maximum power point tracking (MPPT) algorithm based on the combination of improved gray wolf optimizer (gwo) and incremental conductance (INC) methods, aiming to improve the adaptability and stability of PV systems in complex environments. By introducing innovative measures such as a candidate point dynamic focusing mechanism, position updates with perturbation factors, a five-level dynamic step-size strategy, direction consistency detection, and momentum suppression, the algorithm improves the search efficiency of the gwo and its adaptability to environmental mutations, avoids the drawbacks of fixed step sizes, and reduces overshoot and oscillations. Simulation verification was carried out on a simulation platform. The simulation results show that under various operating conditions, the algorithm achieves a good adaptive balance between global exploration and local exploitation, with tracking efficiency consistently above 99%, significantly improving the accuracy and efficiency of maximum power point tracking.

关键词： PV system MPPT control algorithm gwo algorithm INC algorithm enhanced gwo-INC algorithm shadow condition

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A new hybrid framework for medical image retrieval and compression using neural networks

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第4期28卷 1-25页

作者： Khalifeh, Mohammad Hossein Taghizadeh, Mehdi Ghanbarian, Mohammad Mehdi Jamali, Jasem Islamic Azad Univ Dept Elect & Comp Kazerun Branch Kazerun Iran

With the rapid growth of medical image data, there is an increasing need for real-time uploading and storage of these images despite limitations in network bandwidth and storage capacity. However, existing medical image compression technologies are unable to achieve joint optimization of compression rate and image quality (referred to as "rate-distortion" optimization). The primary goal of this study is to maximize the compression of medical images while ensuring high-quality reconstruction. This study utilizes two methods for compressing medical images intended for long-term storage. In the first approach, images are simplified through a hierarchical model that classifies image elements using a neural network. These classified images are then compressed using Huffman coding. The second method employs a prediction-based approach involving deep neural network training. By utilizing a trained neural network, this approach reduces data requirements by estimating pixel positions, with the remaining data compressed using Huffman coding. To reconstruct the images, the compressed data is decoded, and an enhanced spatial filtering technique is applied, followed by reconstruction using the Wild Horse Optimization (WHO) and Gray Wolf Optimization (gwo) meta-heuristic algorithms. This method allows the proposed strategies to function effectively in non-deterministic environments without compromising compression efficiency. The proposed methods successfully simplify medical images, resulting in improved decoding speeds. We achieved a Structural Similarity Index (SSIM) of 0.9637 and a Peak Signal-to-Noise Ratio (PSNR) of 38.196, both indicating high reconstruction quality. Performance metrics were then analyzed in the context of daily practical applications. Compared to existing deep learning-based approaches, our proposed methods provide high levels of compression while preserving excellent image quality.

关键词： Medical image retrieval Medical image compression gwo algorithm Neural network WHO algorithm

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Optimization of a Novel FOPIDN-(1+PIDN) Controller for Renewable Integrated Multi-Area Load Frequency Control System With Non-Linearities

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IEEE ACCESS 2025年 13卷 56736-56755页

作者： Ojha, Shreekanta Kumar Obaiah, Maddela Chinna Vellore Inst Technol Sch Elect Engn Vellore 632014 Tamil Nadu India

The evolution of multi-regional interconnected power systems has been driven by the swift technological advancements of power-producing technologies and the increasing demand from power consumers. To meet the growing power demand, the power system relies on renewable energy sources, but their unpredictable nature influences the dynamic performance of the power system, especially frequency regulation. In response to this challenge, a robust cascade controller called FOPIDN (1+PIDN) has been recommended for the Load Frequency Control (LFC) system. This system encompasses two distinct areas that integrate a non-reheat thermal system with renewable energy sources such as solar photovoltaic and wind energy systems. Additionally, the study has introduced nonlinear constraints like Governor Dead Band (GDB) and Generation Rate Constraints (GRC) to enhance the realism of the analysis. The optimal parameters for the controller have been fine-tuned using the Grey Wolf optimization (gwo) technique by minimizing the ITAE performance index. The findings unequivocally show better performance in terms of performance indices, settling time, overshoots, and undershoots of the proposed controller over existing controllers such as PID, PIDN, FOPID, and FOPIDN. At last, a robustness study has been conducted to validate the efficiency of the proposed controller, assessing its performance across different load scenarios and under extreme variations in system parameters.

关键词： Power system stability Optimization Frequency control Renewable energy sources Power systems Power system dynamics Load modeling Thermal stability Genetic algorithms Resilience FOPIDN-(1+PIDN) cascade controller generation rate constraints (GRC) governor dead band (GDB) gwo algorithm load frequency control (LFC) renewable energy sources (RES) non-linearities optimization techniques sensitivity analysis

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Grey Wolf algorithm-Based source size and location determination method for capacity expansion planning in power systems

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2025年 61卷

作者： Fesli, Ugur Ozdemir, Mustafa Bahadir Akin, Murat Gazi Univ Fac Technol Dept Energy Syst Engn Ankara Turkiye Gazi Univ Fac Engn Dept Comp Engn Ankara Turkiye

Expansion of power generation capacity within power systems is crucial for improving system reliability and efficiency. The main objective of the process is to optimize the power generation capacity to appropriately meet current and future load demands. In this study, the stability issues that arise in power systems due to the increment in load power, overloads on transmission lines, changes in bus voltages and the effects of available generation resources on these dynamics are investigated. In particular, the issues such as overloads on transmission lines, increases in the loading rates of generation resources and decreases or increases in bus voltages are examined. The size and location of new sources to be added to the system have great importance in order to overcome these issues. IEEE 39 bus test system is used, and the power system is modeled in the DIgSILENT platform. Integrated simulations were performed by integrating the DIgSILENT and Python programming. The simulation scenarios are created by increasing the load power located at various buses. The scenarios and system configurations were automatically tested through Python scripts, allowing a broader perspective of the effects of line loading, bus voltages and generation resources on system performance. The possible solutions without optimization are obtained in the first stage with the standard IEEE 39 bus test system. Considering the source size and location, line loading limits, generator capacity, and bus parameters, the Grey Wolf optimization algorithm determines the optimum solution for the capacity expansion. Thus, an effective method is proposed for power system planning. According to the optimization results the IEEE 39 bus test system is modified by increasing capacity of current sources or adding new sources. The obtained solutions with the proposed method are simulated, and the system performance in terms of line loading, generator loading, and bus voltage is tested. The results reveal that th

关键词： gwo algorithm Power system stability Power generation Capacity expansion Line loading

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Optimizing fracture toughness estimation for rock structures: A soft computing approach with gwo and IWO algorithms

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MEASUREMENT 2024年 238卷

作者： Fattahi, Hadi Ghaedi, Hossein Armaghani, Danial Jahed Arak Univ Technol Fac Earth Sci Engn Arak Iran Univ Technol Sydney Sch Civil & Environm Engn Sydney NSW 2007 Australia

The importance of mode I fracture toughness (KIc) K Ic ) lies in its role in studying rock fractures, finding application across diverse sectors like mining, tunneling, and surface construction. Given the time-consuming, costly, and arduous nature of determining fracture toughness through lab and field tests, employing indirect techniques is advised. Soft computing's indirect methods excel at modeling intricate nonlinear connections with optimal efficiency and accuracy, even when geological parameters introduce uncertainty. The innovative aspect of this research lies in leveraging Grey Wolf Optimization (gwo) and Invasive Weed Optimization (IWO) algorithms to model 88 Chevron Notched Brazilian Disc (CCNBD) specimens, designated by ISRM, considering uncertainties. The input parameters encompass uniaxial tensile strength (6t), t ), initial crack length (alpha 0), 0 ), disc specimen radius (R), R ), crack length (alpha B), B ), disc thickness (B), B ), and final crack length (alpha 1). 1 ). The research proceeds by employing 80% of the dataset (70 data points) for constructing models via the algorithms, reserving the remaining 20% (18 data points) for model validation. The outcomes, evaluated using statistical metrics such as mean square error (MSE), squared correlation coefficient (R2), mean absolute error (MAE), and root mean square error (RMSE) underscore the exceptional accuracy and performance of gwo and IWO algorithms in predicting and estimating rock structure's KIc. Ic . Moreover, this paper conducts a sensitivity analysis on K I c estimation's input parameters using @RISK software. Findings reveal that among the input parameters, 6t t exerts the most substantial influence on the model's output. Consequently, even a minor alteration in 6t t yields significant fluctuations in the relationship's output.

关键词： Fracture Toughness Rock Structures gwo algorithm IWO algorithm Sensitivity Analysis

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A Three-Phase Multilevel Inverter Synthesized with 31 Levels and Optimal Gating Angles Based on the GA and gwo to Supply a Three-Phase Induction Motor

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ENERGIES 2024年第5期17卷 1267页

作者： Hussein, Taha Ahmad Ishak, Dahaman Tarnini, Mohamad Northern Tech Univ Dept Elect Power Engn Mosul 41002 Iraq USM Offshore Ctr TR-34500 Istanbul Turkiye Univ Sains Malaysia Sch Elect & Elect Engn Nibong Tebal 14300 Malaysia Beirut Arab Univ Dept Elect & Comp Engn Beirut 11072809 Lebanon

A three-phase multilevel inverter (MLI), synthesized with 31 levels in regard to its output voltage, is used to provide the AC supply to a three-phase, squirrel cage induction motor. The gating angles required for the 30 power switches on the MLI are optimized using both the genetic algorithm (GA) and the grey wolf optimizer (gwo), in which the optimal angles are determined through solving the trigonometric equations taken from Fourier analysis to target the minimum total harmonic distortion (THD) at the MLI output. A simulation model and an experimental prototype are developed for performance analysis and validation. The results demonstrate that the MLI is effectively able to produce 31 levels of three-phase AC output voltage, with the THD not exceeding 5% when loaded with a resistive load and a three-phase induction motor. The voltage and current are measured and recorded for different loads and operating conditions, including the amount of energy consumed by the load. The results of the frequency analysis demonstrate that most of the triple harmonics, which can harm the efficiency of the inverter, are cancelled.

关键词： DC/AC (inverter) power converter GA algorithm gwo algorithm induction motor

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Prediction of SO2 concentration in WFGD system based on gwo optimized CNN-BiLSTM-attention

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2024年第0期

作者： Tang, Minan Bai, Zhongcheng Qiu, Jiandong Rao, Chuntao Jiang, Yude Sheng, Wenxin Lanzhou Jiaotong Univ Sch Automat & Elect Engn Lanzhou 730070 Peoples R China Lanzhou Jiaotong Univ Sch Mech Engn Lanzhou Peoples R China

Due to external disturbances, the parameters of the desulphurization system are uncertain, and their relationships are complex, which makes it difficult to predict the concentration of SO2 at the desulphurization system outlet. In this paper, grey wolf optimization (gwo) optimized convolutional neural network (CNN)-bi-directional long short-term memory (BiLSTM)-Attention algorithm was used for prediction, and the problem of low SO2 concentration prediction accuracy at outlet has been resolved. First, the outliers of the thermal power plant desulphurization data were processed using the local outlier factor (LOF) algorithm. Secondly, CNN-BiLSTM model was constructed using CNN and BiLSTM, and attention module was added to realize feature extraction and better capture the regularity of input data. Then, the CNN-BiLSTM-Attention model was optimized using gwo and its hyperparameters were improved. Finally, based on the Matlab R2023a platform, the prediction comparison as well as the error analysis of the desulphurization data were carried out. In the prediction of SO2 concentration in low-flow continuous slurry supply mode, the error of the combined model decreased by 23.2% on average compared to the CNN-BiLSTM-Attention model. In the prediction of SO2 concentration in the high-flow intermittent slurry supply mode, the error of the combined model decreased by 20.8% on average. According to the results, the combined model surpasses both the single model and several other algorithmic combination models in terms of performance metrics, and the predictions are more accurate.

关键词： attention BiLSTM network gwo algorithm pH prediction SO2 prediction

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