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Multijunction solar cell parameter estimation based on metaheuristic algorithms

RESULTS IN ENGINEERING 2025年 25卷

作者： Elzalabani, Marwa M. Atia, Doaa M. Eliwa, Aref Y. Zalam, Belal A. Abou Abouomar, Mahmoud S. Elect Res Inst ERI Photovolta Cells Dept Cairo Egypt Menofeia Univ Ind Elect & Control Engn Dept Menoufia Egypt

Parameter estimation in MJSC achieved accurate energy output modeling under diverse environmental conditions. This issue maximizes energy yield and facilitates early fault detection by identifying performance deviations, enabling timely maintenance, and reliable energy production. MJSCs are highly nonlinear and require precise parameter selection. Several mathematical models exist for MJSC parameter estimation- Single Diode Model (SDM), Double Diode Model (DDM), and Triple Diode Model (TDM)-each offering a trade-off between complexity and accuracy, improved efficiency, and reliable operation. This paper evaluates three models-SDM, DDM, and TDM-focusing on MJSC parameter estimation. The models include five parameters for SDM, seven for DDM, and nine for TDM, with three additional terms for breakdown voltage alongside conventional parameters. In addition, The Newton-Raphson Method (NRM) hybrid with meta-heuristic algorithms, or MOAs, is used to improve solution estimates, accelerate convergence rates, reduce computational costs, and enhance robustness. The study compared the performance of five optimization algorithms: Grey Wolf Optimizer (GWO), Artificial Hummingbird Algorithm (AHA), Tunicate Swarm Algorithm (TSA), War Strategy Algorithm (WSA), and Moth flam optimizer (MFO). The results demonstrate the superior performance of the AHA algorithm, which has the lowest RMSE value (0.0582) in the SDM and DDM cases, while WSA has the lowest RMSE value (0.0488) in the TDM instance. The MFO algorithm outperforms the GWO algorithm in SDM and DDM scenarios, while the reverse is true for TDM. In terms of errors, and execution time, the TSA algorithm performs the poorest in all three circumstances with RMSE value greater than 0.16.

关键词： Multijunction solar cell Mathematical model metaheuristic algorithms Parameter estimation Newton Raphson RMSE

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Enhancing Non-Invasive Blood Glucose Prediction from Photoplethysmography Signals via Heart Rate Variability-Based Features Selection Using metaheuristic algorithms

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algorithms 2025年第2期18卷 95-95页

作者： Alghlayini, Saifeddin Al-Betar, Mohammed Azmi Atef, Mohamed Ajman Univ Coll Engn & Informat Technol Artificial Intelligence Res Ctr AIRC POB 346 Ajman U Arab Emirates Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan United Arab Emirates Univ Dept Elect & Commun Engn Coll Engn Al Ain 15551 U Arab Emirates

Diabetes requires effective monitoring of the blood glucose level (BGL), traditionally achieved through invasive methods. This study addresses the non-invasive estimation of BGL by utilizing heart rate variability (HRV) features extracted from photoplethysmography (PPG) signals. A systematic feature selection methodology was developed employing advanced metaheuristic algorithms, specifically the Improved Dragonfly Algorithm (IDA), Binary Grey Wolf Optimizer (bGWO), Binary Harris Hawks Optimizer (BHHO), and Genetic Algorithm (GA). These algorithms were integrated with machine learning (ML) models, including Random Forest (RF), Extra Trees Regressor (ETR), and Light Gradient Boosting Machine (LightGBM), to enhance predictive accuracy and optimize feature selection. The IDA-LightGBM combination exhibited superior performance, achieving a mean absolute error (MAE) of 13.17 mg/dL, a root mean square error (RMSE) of 15.36 mg/dL, and 94.74% of predictions falling within the clinically acceptable Clarke error grid (CEG) zone A, with none in dangerous zones. This research underscores the efficiency of utilizing HRV and PPG for non-invasive glucose monitoring, demonstrating the effectiveness of integrating metaheuristic and ML approaches for enhanced diabetes monitoring.

关键词： non-invasive blood glucose level (BGL) photoplethysmography (PPG) heart rate variability (HRV) feature selection (FS) metaheuristic algorithms machine learning (ML) improved dragonfly algorithm (IDA) Clarke error grid (CEG) diabetes monitoring

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Machinability evaluation of magnesium composite using response surface methodology and nature-inspired metaheuristic algorithms

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2025年第4期19卷 2607-2627页

作者： Dhinakarraj, C. K. Senthilkumar, N. Palanikumar, K. Deepanraj, B. Adhiparasakthi Engn Coll Melmaruvathur 603319 Tamil Nadu India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Chennai 602105 Tamil Nadu India Sri Sai Ram Inst Technol Chennai 600044 Tamil Nadu India Prince Mohammad Bin Fahd Univ Coll Engn Al Khobar 31952 Saudi Arabia

Hard particles in composite hinder the machining process, thereby increasing cutting temperatures and forces;machining optimization will improve the conditions the industrial sector may reap. The proposed work presents the end-milling machining studies on the fabricated magnesium composite;AZ31 alloy is reinforced with 10 wt% of silicon nitride (Si3N4) using the vacuum stir squeeze casting technique. Central composite design (CCD) of response surface methodology (RSM) was considered to design the experiments. According to observation, the cutting temperature (CT) is mainly influenced by depth of cut (DoC) and feed rate (FR). In contrast, surface roughness (SR) and resultant cutting force (RCF) are strongly impacted by FR and rotational speed (RS). SR and the RCF rise as the RS, FR and DoC increase due to shear stress at tool interface;RCF drastically increases with higher FR, eventually increasing SR of the machined workpiece. Due to high friction at the juncture of hard workpiece and carbide milling tool, CT will be higher. With higher RS and FR, CT is observed to be higher. Desirability-based optimization produces an ideal RS of 1000 rpm, 157.392 mm/rev of FR and 0.651 mm DoC. Furthermore, optimization using nature-inspired metaheuristic algorithms viz., artificial bee colony (ABC), grouped bee (GBA), and the teaching learning-based optimization (TLBO) are done and the obtained optimal conditions are compared. With optimized results from RSM and different algorithms, it is found that the ABC algorithms outperform other approaches. Compared with RSM predicted outputs, a maximum reduction in SR by 13.36%, RCF by 15.35% by the ABC algorithm, and a 35.91% reduction in CT by the GBA algorithm.

关键词： AZ31 composite Silicon nitride Squeeze casting End-milling Desirability analysis metaheuristic algorithms

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Optimization of a price break policy and advertisement effort based non-instantaneous deteriorating inventory problem with partial backlogged via metaheuristic algorithms

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MATHEMATICS AND COMPUTERS IN SIMULATION 2025年 236卷 221-247页

作者： Mondal, Rajan Manna, Amalesh Kumar Akhtar, Md Bhunia, Asoke Kumar Abacus Inst Engn & Management Dept Basic Sci & Humanities Magra 712148 India KIIT Deemed be Univ Sch Appl Sci Dept Math Bhubaneswar 751024 Odisha India Bhairab Ganguly Coll Dept Math Kolkata 700056 India Univ Burdwan Dept Math Burdwan 713104 WB India

Repeated advertisements of an item in different media, displayed stock in a decorated showroom, and also price break policies play a vital role in an inventory control system. Also, the relationship between products' selling price and market demand is conflicting when all other factors are fixed. Thus, the incorporation of advertisement policy and price break facility in a business is more realistic. This work investigates an inventory problem for non-instantaneous deteriorating items with advertisement and displayed inventory level dependent demand under price break facility. Moreover, partially backlogged shortages and transportation cost for replenishing items are considered in the proposed inventory model. Based on the customer's demand and storage space of the shop, several cases and sub-cases are considered. In this study, different metaheuristic algorithms are considered for maximizing the average profit in each scenario. Then, considering one numerical example, the proposed model is justified. Finally, the sensitivity analysis is carried out to investigate the impact of model parameters on the policy of optimality and a fruitful conclusion is drawn.

关键词： Non-instantaneous deterioration Advertisement Price break Transportation cost metaheuristic algorithms

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Prediction of Heat-Treated Wood Adhesive Strength Using BP Neural Networks Optimized by Four Novel metaheuristic algorithms

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FORESTS 2025年第2期16卷 291-291页

作者： Cao, Ying Wang, Wei He, Yan Northeast Forestry Univ Coll Mech & Elect Engn Harbin 150040 Peoples R China

This study integrates the Backpropagation (BP) Neural Network with several optimization algorithms, namely Hippopotamus Optimization (HO), Parrot Optimization (PO), Osprey Optimization Algorithm (OOA), and Goose Optimization (GO), to develop four predictive models for the adhesive strength of heat-treated wood: HO-BP, PO-BP, OOA-BP, and GO-BP. These models were used to predict the adhesive strength of the wood that was heat-treated under multiple variables such as treatment temperature, time, feed rate, cutting speed, and abrasive particle size. The efficacy of the BP neural network models was assessed utilizing the coefficient of determination (R2), error rate, and CEC test dataset. The outcomes demonstrate that, relative to the other algorithms, the Hippopotamus Optimization (HO) method shows better search efficacy and convergence velocity. Furthermore, XGBoost was used to statistically evaluate and rank input variables, revealing that cutting speed (m/s) and treatment time (hours) had the most significant impact on model predictions. Taken together, these four predictive models demonstrated effective applicability in assessing adhesive strength under various processing conditions in practical experiments. The MAE, RMSE, MAPE, and R2 values of the HO-BP model reached 0.0822, 0.1024, 1.1317, and 0.9358, respectively, demonstrating superior predictive accuracy compared to other models. These findings support industrial process optimization for enhanced wood utilization.

关键词： BP neural network metaheuristic algorithms wood bonding strength heat-treated wood wood performance prediction

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Predicting the shield effectiveness of carbon fiber reinforced mortars utilizing metaheuristic algorithms

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CASE STUDIES IN CONSTRUCTION MATERIALS 2025年 22卷

作者： Alyami, Mana Ullah, Irfan Ahmad, Furqan Alabduljabbar, Hisham Najran Univ Coll Engn Dept Civil Engn Najran Saudi Arabia Hohai Univ Dept Civil & Transportat Engn Nanjing 210098 Peoples R China UNHCR Kabul Afghanistan Prince Sattam Bin Abdulaziz Univ Coll Engn Al Kharj Dept Civil Engn Al Kharj 11942 Saudi Arabia

Recent studies on carbon fiber-reinforced mortars for electromagnetic interference (EMI) shielding have predominantly relied on practical experiments to investigate the correlation between shielding effectiveness (SE) and design attributes. However, these experiments are resource intensive. Machine learning (ML) models present a faster, cost-effective alternative for simulating outcomes and exploring various scenarios. This study adopts a novel approach by utilizing hybrid models, which offer greater accuracy than individual or ensemble ML models. Specifically, support vector regression (SVR) was combined with three optimization algorithms: firefly algorithm (FFA), particle swarm optimization (PSO), and grey wolf optimization (GWO) to create hybrid models for estimating the SE of carbon fiber-reinforced mortars. Conventional ML techniques like random forest (RF) and decision tree (DT) were also employed for comparison. A dataset of 346 experimental data sets from existing literature was used to evaluate model performance. The SVRPSO hybrid model demonstrated superior performance, achieving the highest coefficient of determination (R2) value of 0.994, compared to SVR-FFA (0.964) and SVR-GWO (0.929). Model interpretability methods identified the aspect ratio (AR) as the most influential parameter, showing that shielding effectiveness (SE) increases significantly with fiber content (FC) up to 0.7 %, after which it stabilizes, with a linear correlation between SE and AR. A user-friendly interface was developed for instant SE prediction of carbon fiber reinforced mortar, requiring only essential input parameters.

关键词： Electromagnetic radiation Shielding effectiveness metaheuristic algorithms Machine learning Fiber-reinforced mortars

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Optimizing task offloading with metaheuristic algorithms across cloud, fog, and edge computing networks: A comprehensive survey and state-of-the-art schemes

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2025年 45卷

作者： Rahmani, Amir Masoud Haider, Amir Khoshvaght, Parisa Gharehchopogh, Farhad Soleimanian Moghaddasi, Komeil Rajabi, Shakiba Hosseinzadeh, Mehdi Natl Yunlin Univ Sci & Technol Future Technol Res Ctr Yunlin Taiwan Sejong Univ Dept Artificial Intelligence & Robot Seoul 05006 South Korea Duy Tan Univ Inst Res & Dev Da Nang Vietnam Duy Tan Univ Sch Engn & Technol Da Nang Vietnam Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh Iran Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan Duy Tan Univ Sch Comp Sci Da Nang Vietnam Jadara Univ Res Ctr Irbid Jordan

The Internet of Things (IoT) significantly impacts various industries, enabling better connectivity and real-time data exchange for applications ranging from smart cities to healthcare. Integrating cloud, fog, and edge computing is essential for managing increased data and processing needs as IoT networks become complex. Cloud computing provides extensive storage and powerful computing capabilities but can experience delays due to the distance data must travel. Fog computing addresses these delays by processing data closer to its source, while edge computing reduces them even further by processing data directly on IoT devices. Effective management of these computing layers requires strategic task offloading, which involves moving tasks to the most appropriate computing layer to balance latency, energy consumption, and operational efficiency. Several strategies have been developed to optimize network communication and task offloading, with metaheuristic algorithms emerging as promising approaches. Inspired by natural processes, these algorithms are skilled at searching complex spaces to find near-optimal solutions for efficient and dynamic task offloading. This review provides a detailed analysis of how metaheuristic algorithms optimize task offloading. It evaluates their effectiveness in improving system performance, managing resources, and reducing costs. The review also identifies the current challenges in this area and suggests future research directions to advance this field.

关键词： Offloading metaheuristic algorithms Cloud computing Fog computing Edge computing

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metaheuristic algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

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IEEE ACCESS 2021年 9卷 26766-26791页

作者： Agrawal, Prachi Abutarboush, Hattan F. Ganesh, Talari Mohamed, Ali Wagdy Natl Inst Technol Hamirpur Dept Math & Sci Comp Hamirpur 177005 India Taibah Univ Elect Engn Dept Coll Engn Medina 42353 Saudi Arabia Cairo Univ Fac Grad Studies Stat Res Operat Res Dept Giza 12613 Egypt Nile Univ Sch Engn & Appl Sci Wireless Intelligent Networks Ctr WINC Giza 16453 Egypt

Feature selection is a critical and prominent task in machine learning. To reduce the dimension of the feature set while maintaining the accuracy of the performance is the main aim of the feature selection problem. Various methods have been developed to classify the datasets. However, metaheuristic algorithms have achieved great attention in solving numerous optimization problem. Therefore, this paper presents an extensive literature review on solving feature selection problem using metaheuristic algorithms which are developed in the ten years (2009-2019). Further, metaheuristic algorithms have been classified into four categories based on their behaviour. Moreover, a categorical list of more than a hundred metaheuristic algorithms is presented. To solve the feature selection problem, only binary variants of metaheuristic algorithms have been reviewed and corresponding to their categories, a detailed description of them explained. The metaheuristic algorithms in solving feature selection problem are given with their binary classification, name of the classifier used, datasets and the evaluation metrics. After reviewing the papers, challenges and issues are also identified in obtaining the best feature subset using different metaheuristic algorithms. Finally, some research gaps are also highlighted for the researchers who want to pursue their research in developing or modifying metaheuristic algorithms for classification. For an application, a case study is presented in which datasets are adopted from the UCI repository and numerous metaheuristic algorithms are employed to obtain the optimal feature subset.

关键词： Feature extraction Search problems Biomedical imaging Task analysis Particle swarm optimization Measurement Machine learning algorithms Binary variants classification feature selection literature review metaheuristic algorithms

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metaheuristic algorithms for PV parameter identification: A comprehensive review with an application to threshold setting for fault detection in PV systems

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2018年第Part3期82卷 3503-3525页

作者： Pillai, Dhanup S. Rajasekar, N. VIT Univ Sch Elect Engn SELECT SERC Vellore Tamil Nadu India

Precise model parameters being the prerequisite for realizing accurate PV models, parameter identification techniques have gained immense interest over the years among the researchers specializing in PV systems. The application of various promising metaheuristic algorithms to optimize the model parameters have lightened up the scope of further enhancements in this field. Ever since, numerous metaheuristic algorithms have deployed for this purpose. With handful of techniques available in this regard, this paper takes up an initiative to review the existing metaheuristic algorithms based parameter extraction techniques with an emphasis on their compatibility, accuracy, convergence speed, range of parameters set and their validating environment. Based on the analysis conducted, accurate models available for 17 different industrial solar cells/modules are identified. Inspired by this review, an unidentified gateway between parameter extraction and fault detection in PV systems have been identified;and has further extended this review to differentiate some models that can help the researchers to achieve accurate, efficient and rapid fault detection. This review is a valuable gathering of statistics from the various researches carried out in PV parameter extraction which can assist enhanced researches for fault detection in PV systems as well.

关键词： Parameter extraction Fault detection PV metaheuristic algorithms Optimization techniques

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metaheuristic algorithms to allocate and schedule of the rescue units in the natural disaster with fatigue effect

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NEURAL COMPUTING & APPLICATIONS 2019年第11期31卷 7517-7537页

作者： Nayeri, Sina Asadi-Gangraj, Ebrahim Emami, Saeed Babol Noshirvani Univ Technol Dept Ind Engn Babol Sar Iran

During each year, natural disasters like floods, hurricanes, tornadoes, earthquakes, and mass movements cause enormous damages to the people and infrastructure. Designing an effective decision support model to allocate and schedule of the rescue units can reduce economic losses and casualties in the natural disasters. By assuming the incidents as jobs and rescue units as machines, we can formulate the research problem as an unrelated parallel machine scheduling problem. In this paper, a mixed integer linear programming model is proposed to minimize the sum of the weighted completion times and delays at the start of relief operations. After relieving several incidents, rescuers will become tired and then need more time to relieve the remaining incidents which were assigned to them;therefore, we consider this phenomenon as fatigue effect in this research. The rescue units also have different capabilities, and each incident just can be allocated to a rescue unit that is able to do it. Due to NP-hardness of the research problem, three metaheuristic algorithms, namely simulated annealing (SA) algorithm, particle swarm optimization (PSO) algorithm, and a method based on hybrid SA and PSO (SA-PSO), are developed to solve the research problem. Finally, the developed metaheuristic algorithms are ranked by applying the technique for order of preference by similarity to ideal solution. The experimental results illustrate that the SA algorithm and the hybrid SA-PSO are better than others in terms of CPU time and solution quality, respectively.

关键词： Natural disaster management Fatigue effect Unrelated parallel machines scheduling metaheuristic algorithms TOPSIS

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