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Assessment of Unknown Parameters in Photovoltaic Cells Utilizing Multi- Strategy Fusion Slime Mould algorithm

IEEE ACCESS 2024年 12卷 72504-72520页

作者： Chen, Chi Chen, Xian Lin, Aiju Wenzhou Univ Technol Sch Data Sci & Artificial Intelligence Wenzhou 325000 Peoples R China Wenzhou Polytech Wenzhou 325035 Zhejiang Peoples R China Wenzhou Univ Coll Int Educ Wenzhou 325035 Zhejiang Peoples R China

The study presents the Multi-Strategy Learning Fusion Slime Mould algorithm (MFSMA), a novel method designed to optimize parameters in photovoltaic systems, thereby increasing the efficiency of solar energy conversion. MFSMA employs an advanced technique that incorporates elite opposition-based learning to quickly identify optimal solutions while preserving diversity within the population. Additionally, it integrates a ranking mechanism from the grey wolf algorithm, which categorizes individuals according to their fitness levels, ensuring a balanced approach between exploratory diversity and exploitative precision during the optimization journey. Through rigorous testing on various benchmark functions, MFSMA has demonstrated its exceptional ability to outperform existing algorithms widely used in the sector. The algorithm's effectiveness is further validated through its application in determining the parameters of single, double, and triple-junction photovoltaic modules. Moreover, the durability and effectiveness of the MFSMA algorithm have been rigorously evaluated using data from manufacturers' datasheets across different temperature and irradiance conditions. Statistical analysis supports the conclusion that MFSMA offers superior accuracy and dependability in estimating vital parameters for photovoltaic modules, making it an invaluable tool for overcoming the challenges of parameter identification in solar energy technologies.

关键词： Photovoltaic systems Solar power generation Optimization methods Solar energy Batteries Resistance Semiconductor diodes Parameter estimation Photovoltaic models parameter extraction slime mould algorithm multi-strategy learning parameter optimization grey wolf algorithm elite opposition-based learning

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Application of improved grey wolf model in collaborative trajectory optimization of unmanned aerial vehicle swarm

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SCIENTIFIC REPORTS 2024年第1期14卷 1-18页

作者： Chen, Jiguang Chen, Yu Nie, Rong Liu, Li Liu, Jianqiang Qin, Yuxin Zhengzhou Univ Aeronaut Sch Elect & Commun Engn Zhengzhou 450046 Peoples R China Zhengzhou Univ Aeronaut Collaborat Innovat Ctr Aeronaut & Astronaut Elect Zhengzhou 450046 Henan Peoples R China Zhengzhou Univ Aeronaut Henan Key Lab Gen Aviat Technol Zhengzhou 450046 Peoples R China

With the development of science and technology and economy, UAV is used more and more widely. However, the existing UAV trajectory planning methods have the limitations of high cost and low intelligence. In view of this, grey wolf algorithm is being used to achieve collaborative trajectory optimization of UAV groups. However, it is found that the grey wolf optimization algorithm (GWO) has the problem of weak cooperation. In this study, based on the traditional GWO pheromone factor is introduced to improve it.. Aiming at the problem of unstable performance of swarm intelligence optimization algorithm under dynamic threat, deep reinforcement learning is used to optimize the model. An unmanned aerial vehicle swarm trajectory planning model was constructed based on the improved grey wolf algorithm. Through experimental analysis, the optimal fitness value of the improved grey wolf algorithm was lower than 0.43 of the grey wolf algorithm. Compared with other algorithms, the fitness value of this algorithm is significantly reduced and the stability is higher. In complex scenarios, the improved grey wolf algorithm had a trajectory length of 70.51 km and a planning time of 5.92 s, which was clearly superior to other algorithms. The path length planned by the research and design model was 58.476 km, which was significantly smaller than the other three models. The planning time was 5.33 s and the number of path extension points was 46. The indicator values of the Unmanned Aerial Vehicle swarm trajectory planning model designed by this research were all smaller than the other three models. By analyzing the results, the model can achieve low-cost trajectory optimization, providing more reasonable technical support for unmanned aerial vehicle mission execution.

关键词： grey wolf algorithm Deep reinforcement learning Unmanned aerial vehicle Track planning Swarm intelligence optimization

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Prediction of bond strength of reinforced concrete structures based on feature selection and GWO-SVR model

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CONSTRUCTION AND BUILDING MATERIALS 2023年第1期400卷

作者： Fan, Congcong Zheng, Yuanxun Wang, Shaoqiang Ma, Junjie Zhengzhou Univ Sch Water Conservancy & Civil Engn Zhengzhou 450001 Peoples R China Zhengzhou Univ Yellow River Lab Zhengzhou 450001 Peoples R China

Bond strength, as a mechanical property of reinforced concrete (RC) structures, is a crucial factor affecting the force characteristics of RC structures. In order to assess the load-bearing and deformation capacity of RC structures, it is essential to develop a model that can accurately predict the bond strength of RC structures. Therefore, a prediction model based on Random Forest (RF) feature selection and grey wolf algorithm optimized (GWO) support vector regression (GWO-SVR) is synthesized in this paper. An extensive database containing 1008 bonded slip test samples was first collected, and various feature parameters were finally preprocessed to filter 935 sets of test data. Next, the optimized support vector machine was trained using the training set data using the GWO-optimized SVR method. Meanwhile, the prediction accuracy of the model was evaluated using the integrated absolute error (IAE), the coefficient of determination (R2), and the mean absolute error (MAE). The model with a good prediction effect can predict the bond strength of unknown tests. When the sample size is small and the number of features is extensive, redundant features must be filtered out before the model prediction. The model prediction after feature selection is significantly better than that before training. The results show that the GWO-SVR model proposed in this study has a higher prediction accuracy than the existing bond strength model. The prediction results of the GWO-SVR model (Test set: R2 = 0.9547, MAE = 1.2487 MPa, IAE = 12.761%) outperform benchmark models such as the multilayer perceptron (MLP) and linear regression (LR). According to the training accuracy of the regression model, it can be found that the GWO-SVR model has good generalization performance (Training set: R2 = 0.9562, MAE = 1.1203 MPa, IAE = 12.182%). In addition, different optimization-seeking algorithms, kernel function types, and machine learning models have significant effects on the prediction results of

关键词： Reinforced concrete structure Bond tests Random forest feature selection grey wolf algorithm Support vector regression prediction

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Optimization of virtual closed-loop supply chain under uncertainty application of IoT

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KYBERNETES 2023年第5期52卷 1745-1777页

作者： Shambayati, Hanieh Nikabadi, Mohsen Shafiei Firouzabadi, Seyed Mohammad Ali Khatami Rahmanimanesh, Mohammad Saberi, Sara Semnan Univ Fac Econ Management & Adm Sci Dept Ind Management Semnan Iran Allameh Tabatabai Univ Fac Management & Accounting Dept Ind Management Tehran Iran Semnan Univ Fac Elect & Comp Engn Dept Comp Engn Semnan Iran Worcester Polytech Inst Worcester MA 01609 USA

Purpose - Supply chains (SCs) have been growingly virtualized in response to the market challenges and opportunities that are presented by new and cost-effective internet-based technologies today. This paper designed a virtual closed-loop supply chain (VCLSC) network based on multiperiod, multiproduct and by using the Internet of Things (IoT). The purpose of the paper is the optimization of the VCLSC network. Design/methodology/approach - The proposed model considers the maximization of profit. For this purpose, costs related to virtualization such as security, energy consumption, recall and IoT facilities along with the usual costs of the SC are considered in the model. Due to real-world demand fluctuations, in this model, demand is considered fuzzy. Finally, the problem is solved using the grey wolf algorithm and Firefly algorithm. A numerical example and sensitivity analysis on the main parameters of the model are used to describe the importance and applicability of the developed model. Findings - The findings showed that the Firefly algorithm performed better and identified more profit for the SC in each period. Also, the results of the sensitivity analysis using the IoT in a VCLSC showed that the profit of the virtual supply chain (VSC) is higher compared to not using IoT due to tracking defective parts and identifying reversible products. In proposed model, chain members can help improve chain operations by tracking raw materials and products, delivering products faster and with higher quality to customers, bringing a new level of SC efficiency to industries. As a result, VSCs can be controlled, programmed and optimized remotely over the Internet based on virtual objects rather than direct observation. Originality/value - There are limited researches on designing and optimizing the VCLSC network. This study is one of the first studies that optimize the VSC networks considering minimization of virtual costs and maximization of profits. In most researches, the t

关键词： Virtual closed-loop supply chain (VCLSC) Optimization Internet of things (IoT) Traceability Firefly algorithm grey wolf algorithm Uncertainty

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A novel intelligent deep optimized framework for heart disease prediction and classification using ECG signals

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第12期83卷 34715-34731页

作者： Goud, P. Satyanarayana Sastry, Panyam Narahari Sekhar, P. Chandra G Narayanamma Inst Technol & Sci for Women Fac Elect & Commun Engn Hyderabad 500104 Telangana India Chaitanya Bharathi Inst Technol CBIT Dept Elect & Commun Engn Hyderabad 500075 Telangana India Osmania Univ Dept Elect & Commun Engn Hyderabad 500007 Andhra Pradesh India

The advancement in medical diagnosis approaches increases the demand for effective disease prediction and classification system. Although, various machine learning (ML) based disease classification techniques were developed, they face severe issues. Hence, a novel optimized framework named as wolf based Generative Adversarial System (WbGAS) system was designed to predict and specify the heart disease using Electrocardiogram (ECG) database. The collected dataset contains three classes namely Normal Sinus Rhythm (NSR), Arrhythmia (ARR), and Congestive Heart Failure (CHF). The dataset is initialized and trained using the proposed (WbGAS) approach to predict the normal and abnormal signals present in dataset. In addition, the integration of wolf fitness function in the presented approach provides finest prediction rate. Moreover, the type of heart disease is specified based on the trained features. Also, a case study was presented with three different cases to explain the functioning of designed (WbGAS) approach. The designed model is implemented in MATLAB software and then, the performance of the system is determined as specificity, recall, accuracy, and precision value. At the end, to verify the results of the developed technique a comparative assessment was performed by comparing the outcomes of presented approach with existing ML based approaches.

关键词： Heart disease classification grey wolf algorithm Generative adversarial network Arrhythmia Heart failure

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Carbon market price prediction based on sequence decomposition-reconstruction-dimensionality reduction and improved deep learning model

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JOURNAL OF CLEANER PRODUCTION 2023年 425卷

作者： Wang, Huaqing Tan, Zhongfu Zhang, Amin Pu, Lei Zhang, Junjian Zhang, Zhi North China Elect Power Univ Sch Econ & Management Baoding 102206 Peoples R China Yanan Univ Yanan 716000 Shaanxi Peoples R China Cent Southern China Elect Power Design Inst Co Ltd China Power Engn Consulting Grp Wuhan 430064 Peoples R China Shanxi Elect Power Co SGCC Econ & Elect Res Inst Taiyuan 30021 Peoples R China

Identifying carbon market price signals and accurately predicting carbon prices is of great significance in the development of carbon markets and carbon trading management. In this study, a fast ensemble empirical mode decomposition (FEEMD) method was used to analyze the fluctuation patterns of carbon market prices and obtain their characteristic components. Then the characteristic components of carbon prices were reconstructed using sample entropy theory (SRSE), and a partial autocorrelation analysis (PACF) technique was used to screen the data points exerting large impacts on carbon market prices. The FEEMD-SRSE-PACF-ILSTM hybrid model constructed. The main innovation points are as follows: The FEEMD is proposed to solves the common modal aliasing problem during carbon price decomposition and improves the efficiency of the carbon price sequence decomposition;the sample entropy theory and partial autocorrelation analysis methods are applied to reconstruct the characteristic components of carbon prices;the LSTM neural network model was optimized by the grey wolf algorithm to enhance its stability and adaptability. The analyzed price changes of large regional carbon markets in China, the European Union, and the United States can be effectively used to predict prices in the carbon market and guide its participants towards low-carbon investment and trading.

关键词： Carbon price prediction Sequence reconstruction grey wolf algorithm Enhanced deep learning model

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A novel hybridized feature selection strategy for the effective prediction of glaucoma in retinal fundus images

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第15期83卷 46087页

作者： Singh, Law Kumar Khanna, Munish Thawkar, Shankar Singh, Rekha GLA Univ Dept Comp Engn & Applicat Mathura 281406 India Hindustan Coll Sci & Technol Dept Comp Sci & Engn Mathura 281122 India Hindustan Coll Sci & Technol Dept Informat Technol Mathura 281122 India Uttar Pradesh Rajarshi Tandon Open Univ Dept Phys Prayagraj 211021 Uttar Pradesh India

Feature selection (FS) is crucial to transforming high-dimensional data into low-dimensional data. The FS approach selects influential traits and ignores the rest. This approach improves machine learning (ML) classifiers by reducing computational complexity and solution time. This empirical study presents a novel and effective methodology that uses two contemporary state-of-the-art soft-computing algorithms, the grey wolf Optimizer (GWO) and the Whale Optimization algorithm (WOA). We have also created the hybrid version (hGWWO) of these two approaches as our novel, innovative scientific contribution. The baseline algorithms above have been used previously for feature selection across different domains. According to our understanding, these three algorithms are being used for the first time in glaucoma identification, particularly on the publicly available benchmark dataset, ORIGA. The rising global prevalence of glaucoma prompted this proposed methodology's focus on the illness. This illness is second only to cataracts in causing visual loss. Medical imaging professionals are examining retinal scans to diagnose glaucoma. Manual eye screening and retinal fundus imaging for confirmation of this infection require skilled ophthalmologists. The screening analysis method is time-consuming, requires experienced staff, and is subject to observational differences. In order to overcome these issues and to support the medical fraternity, an artificial intelligence-supported computer-aided clinical decision support system (CA-CDSS) is implemented in the present endeavor for confirmation of this disease from retinal fundus images. Nature-inspired computing strategies for feature selection and ML models for classification are employed to classify fundus retinal images under investigation. From the ORIGA dataset, sixty-five features were retrieved. A subset of most influential features is selected from the original dataset using three soft-computing-based FS methods. ML classifier

关键词： Glaucoma prediction Retinal fundus images grey wolf algorithm Whale optimization algorithm Hybrid approach

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A novel economic dispatch in the stand-alone system using improved butterfly optimization algorithm

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ENERGY STRATEGY REVIEWS 2023年 49卷

作者： Alhasnawi, Bilal Naji Jasim, Basil H. Bures, Vladimir Sedhom, Bishoy E. Alhasnawi, Arshad Naji Abbassi, Rabeh Alsemawai, Majid Razaq Mohamed Siano, Pierluigi Guerrero, Josep M. Al Furat Al Awsat Tech Univ Al Samawah Tech Inst Dept Elect Tech Al Samawah 66001 Iraq Basrah Univ Elect Engn Dept Basrah 61001 Iraq Univ Hradec Kralove Fac Informat & Management Hradec Kralove 50003 Czech Republic Mansoura Univ Fac Engn Elect Engn Dept Mansoura 35516 Egypt Al Muthanna Univ Coll Educ Pure Sci Dept Dept Biol Samawah 66001 Iraq Univ Hail Coll Engn Dept Elect Engn Hail City 81451 Saudi Arabia Imam Jaafar Al Sadiq Univ Coll Informat Technol Dept Comp Tech Engn Al Muthanna 66002 Iraq Salerno Univ Management & Innovat Syst Dept I-84084 Salerno Italy Univ Johannesburg Dept Elect & Elect Engn Sci ZA-2006 Johannesburg South Africa Aalborg Univ AAU Energy Dept Ctr Res Microgrids CROM DK-9220 Aalborg Denmark

Distributed renewable energy systems are now widely installed in many buildings, transforming the buildings into 'electricity prosumers'. Additionally, managing shared energy usage and trade in smart buildings continues to be a significant difficulty. The main goal of solving such problems is to flatten the aggregate power consumption-generation curve and increase the local direct power trading among the participants as much as possible. This study provides a coordinated smart building energy-sharing concept for smart neighborhood buildings integrated with renewable energy sources and energy storage devices within the building itself. This neighborhood energy management model's primary objective is to reduce the total power cost of all customers of smart buildings in the neighborhood by increasing the use of locally produced renewable energy. In the first stage, a group of optimum consumption schedules for each HEMS is calculated by an Improved Butterfly Optimization algorithm (IBOA). A neighborhood energy management system (NEMS) is established in the second stage based on a consensus algorithm. A group of four smart buildings is used as a test system to evaluate the effectiveness of the suggested neighborhood smart building energy management model. These buildings have varying load profiles and levels of integration of renewable energy. In this paper, the proposed framework is evaluated by comparing it with the grey wolf optimization (GWO) algorithm and W/O scheduling cases. With applying GWO, the total electricity cost, peak load, PAR, and waiting time are improved with 3873.723 cents, 21.6005 (kW), 7.162225 (kW), and 87 s respectively for ToU pricing and 11217.57 (cents), 18.0425(kW), 5.984825 (kW), and 98 s respectively for CPP tariff. However, using the IBOA Improves the total electricity cost, peak load, PAR, and waiting time by 3850.61 (cents), 20.1245 (kW), 6.7922 (kW), and 53 s respectively, for ToU and 10595.8 (cents), 17.6765(kW), 5.83255(kW), and 74 s f

关键词： Butterfly optimization algorithm grey wolf algorithm Solar energy Wind turbine Demand shifting

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Interpretation of seizure dynamics using fuzzy-based neural computational modelling

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SOFT COMPUTING 2023年 1-15页

作者： Prathaban, Banu Priya Rajendran, Subash Ganeshkumar, N. Gayatri, M. Chembian, W. T. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T Dept ECE Chennai India SRM Inst Sci & Technol Sch Comp Dept Comp Technol Chennai India Gandhi Inst Technol & Management GITAM Sch Technol Dept CSE Bengaluru India Vel Tech High Tech Dr Rangarajan Dr Sakunthala Eng Dept CSE Chennai India

The epileptic seizures result due to the nonlinear dynamic asynchronization of the brain waves. The temporal dynamics of the brain waves are usually depicted from long-term EEG signals. Prediction of seizures craves for the capacity to reliably recognize the distinct preictal state comprehended from the remaining seizure states. The differentiation between the preictal and interictal states is the key challenge in such a prediction problem, which intently requires the least number of false alarms. Computational models trace the seizure dynamics and descend the segment changeover of the seizure states from interictal toward preictal, preictal toward ictal and ictal toward postictal. In this work, a model driven-computational approach for interpreting the nonlinear temporal dynamics of the four different seizure states is developed. An Augmented Wendling Model for generating type 1 to 10 seizure-like EEG waveforms is established. Tracking and prediction of artificially generated seizure-like EEG waveforms are done via an Optimized Model Driven approach using grey wolf optimizer. Then, the seizure-like EEG waveforms are controlled, using an fuzzy-based neural computational modelling, comprising of Optimized Forward Back Propagation-based Closed Loop Controller (OFBP-CLC) is implemented. Ordered Fuzzy Number System (OFNS) is applied on to the neurons of the OFBP-CLC approach to reduce the complexity of the learning approach. The pattern driven method in concurrence with a computational model is established. Epileptic seizure states are modelled, tracked, predicted and controlled using an arithmetic ordered fuzzy number system.

关键词： Augmented Wendling model Epileptic seizures Fuzzy-based neural computational modelling grey wolf algorithm Optimized model driven approach Ordered fuzzy number system

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A CRANE GEARBOX FAULT DIAGNOSIS METHOD INCORPORATING SVM AND MULTI-CASE MIGRATION LEARNING

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MECHATRONIC SYSTEMS AND CONTROL 2024年第2期52卷 91-100页

作者： Zheng, Xin Zibo Vocat Inst Sch Intelligent Mfg Zibo 255300 Peoples R China

Reducer is the core component of a crane. The health is directly related to the safe operation of the whole crane. Due to the high load work content of cranes, the reducer becomes the main faulty component. The crane reducer vibration signal is difficult to extract and the hidden information is difficult to mine. Therefore, in this study, a method for extracting gearbox fault features based on the grey wolf algorithm combined with variational modal decomposition is proposed. Aiming at the less vibration signals of the reducer fault, a single-case migration learning fault diagnosis model is proposed. For the unstable performance of the diagnostic model using single-case source domain data samples migration training, a multi-case migration learning fault diagnosis model is proposed. The experimental results indicate that the diagnostic accuracy of the migration learning fault diagnostic model in single working condition is more than 90%. The diagnostic accuracy of the migration learning fault diagnostic model in multiple working conditions is more than 90%. The computation time is less than that of other algorithmic models. The results indicate that the designed fault diagnosis model has certain reference significance for the fault diagnosis of speed reducer in the actual production environment.

关键词： grey wolf algorithm variational modal decomposition migration learning speed reducer failure diagnosis

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