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Accurate parameters extraction of PEMFC model based on metaheuristics algorithms

ENERGY REPORTS 2021年 7卷 6854-6867页

作者： Diab, Ahmed A. Zaki Ali, Hamdi Abdul-Ghaffar, H., I Abdelsalam, Hany A. Abd El Sattar, Montaser Minia Univ Fac Engn Elect Engn Dept Al Minya 61111 Egypt El Minia High Inst Engn & Technol Dept Elect & Comp Engn El Minia 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 Kafrelsheikh Univ Fac Engn Elect Engn Dept Kafr Al Sheikh 33516 Egypt South Valley Univ Fac Engn Dept Elect Engn Qena 83523 Egypt

The electro-chemical proton exchange membrane fuel cell (PEMFC) is an inventing electrical generator from chemical reaction process as a green energy source. An accurate PEMFC model with its precise parameters should be used to carefully fitting of polarization curve to best study and design of its characteristics and performance. This paper introduces an accurate PEMFC model based on recent metaheuristics algorithms to evaluate precisely the unknown parameters of PEMFC. algorithms of;Whale Optimization Algorithm (WOA), Weighted Differential Evolution Algorithm (WDE), Differential evolution algorithm with strategy adaptation (SADE), Moth-Flame Optimization Algorithm (MFO), adaptive differential evolution with optional external archive (JADE), Improved mine blast algorithm (IMBA), Gray Wolf Optimizer (GWO), Dragonfly algorithm (DA), Differential EVOLUTION ALGORITHM (DE) , Cumulative Population Distribution Information in Differential Evolution (CPIJDE), Differential evolution based on covariance matrix learning (COBIDE), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Bernstain-search differential evolution algorithm (BSD), Backtracking Search Optimization Algorithm (BSA), Bezier Search Differential Evolution Algorithm (BESD), DIFFERENTIAL SEARCH ALGORITHM (DSA) and Bijective DSA (B-DSA), Biogeography-based optimization (BBO);have been applied to estimate model of PEMFC. The verification of the suggested optimizing algorithms is applied on three practical PEMFC stacks of BCS 500-W PEM, 500 W SR-12PEM and 250 W stacks, for different operating conditions. The accuracies of the PEMFC extracted parameters are measured in sum of square errors (SSE) between the results obtained by the optimizing parameters and the test results of the fuel cell stacks in the objective function. Also, the applied methods have been validated as compared results with different research works that were listed in literatures. Moreover, the polarization curves of the applied methods are

关键词： Fuel cell model Parameter extraction metaheuristics algorithms

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metaheuristics Algorithm-Based Optimization for High Conductivity and Hardness CuNi2Si1 Alloy

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MATERIALS 2025年第5期18卷 1060-1060页

作者： Konieczny, Jaroslaw Labisz, Krzysztof Urgun, Satilmis Yigit, Halil Fidan, Sinan Bora, Mustafa Ozgur Atapek, S. Hakan Silesian Tech Univ Fac Transport & Aviat Engn Dept Railway Transport PL-40019 Katowice Poland Kocaeli Univ Fac Aviat & Astronaut Aviat Elect Elect TR-41001 Kocaeli Turkiye Kocaeli Univ Dept Informat Syst Engn TR-41001 Kocaeli Turkiye Kocaeli Univ Fac Aviat & Astronaut Airframe & Powerplant Maintenance TR-41001 Kocaeli Turkiye Kocaeli Univ Fac Engn Dept Met & Mat Engn Lab High Temp Mat Kocaeli Turkiye

The optimization of CuNi2Si1 alloy's mechanical and electrical properties was achieved through a combination of experimental approaches and metaheuristic algorithms. Optimizing hardness and electrical conductivity through a variation in aging temperature (450-600 degrees C) and aging duration (1-420 min) was taken under consideration in the present work. Cold rolling with 50% strain after solution annealing aided in microstructure refinement and accelerated Ni2Si precipitates' development, and property improvement increased. Optimum temperature and holding period were 450 degrees C and 30 min, respectively, with 266 HV and 13 MS/m and 167 HV and 11.2 MS/m for non-deformed samples, respectively. SPBO, genetic algorithm (GA), and particle swarm optimization (PSO) metaheuristic algorithms were considered, and SPBO exhibited the best prediction accuracy. SPBO predicted 450 degrees C for 61.75 min, and experimental testing exhibited 267 HV and 14 MS/m, respectively. Polynomial regressions with 0.98 and 0.96 values for R-2 confirmed these values' accuracy. According to this work, computational optimization proves effective in optimizing development and property tailoring for application in industries including aerospace and electrical engineering.

关键词： CuNi2Si1 alloy aging electrical conductivity hardness metaheuristics algorithms optimization

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Intrusion detection in metaverse environment internet of things systems by metaheuristics tuned two level framework

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SCIENTIFIC REPORTS 2025年第1期15卷 1-31页

作者： Antonijevic, Milos Zivkovic, Miodrag Jovicic, Milica Djuric Nikolic, Bosko Perisic, Jasmina Milovanovic, Marina Jovanovic, Luka Abdel-Salam, Mahmoud Bacanin, Nebojsa Singidunum Univ Belgrade 11000 Serbia Univ Belgrade Innovat Ctr Sch Elect Engn Belgrade 11000 Serbia Univ Belgrade Sch Elect Engn Belgrade 11000 Serbia Mansoura Univ Fac Comp & Informat Sci Mansoura 35516 Egypt SIMATS Saveetha Sch Engn Chennai 602105 Tamil Nadu India Sinergija Univ Bijeljina 76300 Bosnia & Herceg

Internet of Things (IoT) is one of the most important emerging technologies that supports Metaverse integrating process, by enabling smooth data transfer among physical and virtual domains. Integrating sensor devices, wearables, and smart gadgets into Metaverse environment enables IoT to deepen interactions and enhance immersion, both crucial for a completely integrated, data-driven Metaverse. Nevertheless, because IoT devices are often built with minimal hardware and are connected to the Internet, they are highly susceptible to different types of cyberattacks, presenting a significant security problem for maintaining a secure infrastructure. Conventional security techniques have difficulty countering these evolving threats, highlighting the need for adaptive solutions powered by artificial intelligence (AI). This work seeks to improve trust and security in IoT edge devices integrated in to the Metaverse. This study revolves around hybrid framework that combines convolutional neural networks (CNN) and machine learning (ML) classifying models, like categorical boosting (CatBoost) and light gradient-boosting machine (LightGBM), further optimized through metaheuristics optimizers for leveraged performance. A two-leveled architecture was designed to manage intricate data, enabling the detection and classification of attacks within IoT networks. A thorough analysis utilizing a real-world IoT network attacks dataset validates the proposed architecture's efficacy in identification of the specific variants of malevolent assaults, that is a classic multi-class classification challenge. Three experiments were executed utilizing data open to public, where the top models attained a supreme accuracy of 99.83% for multi-class classification. Additionally, explainable AI methods offered valuable supplementary insights into the model's decision-making process, supporting future data collection efforts and enhancing security of these systems.

关键词： Metaverse CatBoost LightGBM Optimization metaheuristics algorithms Chimp optimization algorithm

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IBBA: an improved binary bat algorithm for solving low and high-dimensional feature selection problems

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2025年 1-31页

作者： Wang, Tao Xie, Minzhu Hunan Normal Univ Coll Math & Stat Changsha 410081 Peoples R China Minist Educ Key Lab Comp & Stochast Math Changsha 410081 Peoples R China Hunan Normal Univ Coll Informat Sci & Engn Changsha 410081 Peoples R China

Technological advancements have resulted in the accumulation of vast amounts of data across various industries, often containing redundant or irrelevant features. As a result, the development of efficient feature selection methods has become increasingly critical. This paper proposes an Improved Binary Bat Algorithm (IBBA) to overcome the limitations of the original Bat Algorithm (BA), particularly its weak exploration ability and tendency to become trapped in local optima. IBBA enhances both exploration and exploitation through a novel Fitness-based Exploitation Strategy (FES) and an improved Harris Hawks Optimization (HHO). Additionally, random perturbations are introduced during iterations to adjust positions that deviate from the search space, thus preventing ineffective searches. Since the original BA is primarily designed for continuous optimization problems, this study also investigates the effect of four V-shaped transfer functions on the algorithm's performance. Experimental results on 28 datasets with varying dimensionalities (ranging from nine to 12,600 features) demonstrate that IBBA outperforms 12 state-of-the-art metaheuristic algorithms in terms of fitness, accuracy, feature selection ratio, and runtime. Moreover, an analysis of exploration and exploitation shows that IBBA effectively balances these two processes, addressing BA's exploration shortcomings. The Wilcoxon signed-rank test, conducted at a significance level of 0.05, validates the algorithm's effectiveness, revealing that IBBA demonstrates significant advantages in 87.5% of the tests. Finally, comparisons with 14 recently proposed feature selection methods highlight IBBA's competitive classification accuracy. Therefore, this study is expected to make a valuable contribution to solving feature selection problems across datasets with diverse dimensionalities.

关键词： Feature selection metaheuristics algorithms Bat algorithm Mutual information Low and high-dimensional datasets

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Optimizing Arrhenius parameters for multi-step reactions via metaheuristic algorithms

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JOURNAL OF MATHEMATICAL CHEMISTRY 2025年第4期63卷 1065-1092页

作者： Eshaghi, Alireza Pouransari, Zeinab Iran Univ Sci & Technol Dept Mech Engn Tehran Iran

In combustion simulation, the Arrhenius equation is a key tool for modeling multi-step reactions such as propane and methane reactions. It describes a relationship between the reaction rate, temperature, the pre-exponential factor, and activation energy. Applying these parameters outside their validated temperature and pressure ranges, or for unverified reactions, can result in important errors. The present study optimizes the coefficients of the Arrhenius model for multi-step combustion reactions, by utilizing experimental data and advanced optimization techniques. Our methodology incorporates metaheuristics techniques such as least squares minimization, particle swarm optimization, ant colony optimization, the slime mold algorithm, and the whale optimization algorithm. The results indicate that the optimized coefficients significantly improve the predictions while reducing computational time and associated costs. Furthermore, this paper presents a comprehensive comparative analysis of the various optimization techniques utilized and clarifies the advantages and limitations of each technique in the context of Arrhenius equation optimization.

关键词： Arrhenius equation metaheuristics algorithms Optimization Methane reaction Propane reaction

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Exploring Chaotic Binarization Rules in the Binary Fox Optimizer for Complex Optimization Problems

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SN Computer Science 2025年第3期6.0卷 1-20页

作者： Cisternas-Caneo, Felipe Crawford, Broderick Soto, Ricardo Barrera-García, José Becerra-Rozas, Marcelo Giachetti, Giovanni Pontificia Universidad Católica de Valparaíso Valparaíso Chile Universidad Andres Bello Santiago Chile

Combinatorial optimization problems, characterized by their inherent complexity and exponential search spaces, present significant challenges in achieving optimal solutions. Metaheuristic algorithms have emerged as versatile tools to address these challenges, with advancements incorporating hybrid approaches such as chaotic maps to enhance their stochastic behavior. This study investigates the application of the Fox Optimizer (FOX) to the Set Covering Problem (SCP), an NP-hard problem with extensive practical relevance. By implementing standard and chaotic binary configurations of FOX and comparing them with Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO), this research evaluates the impact of binarization rules and chaotic maps on algorithmic performance. Experimental results demonstrate that elitist configurations combined with chaotic maps consistently achieve high-quality solutions, offering accelerated convergence and enhanced exploration capabilities. Additionally, execution time analysis highlights the computational efficiency of chaotic configurations, emphasizing their suitability for large-scale problems. This work underscores the importance of aligning algorithmic strategies with problem characteristics and opens avenues for future exploration of chaotic maps and adaptive methodologies in combinatorial optimization. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Binarization rules Chaotic maps Combinatorial optimization Fox optimizer metaheuristics algorithms

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Pre-uniform measures in the artificial intelligence era

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CURRENT PSYCHOLOGY 2025年 1-15页

作者： Dong, Yixiao Univ Denver Dept Res Methods & Informat Sci Denver CO 80208 USA

Psychological measures allow researchers and psychological professionals to capture and quantify the latent attributes of human beings. Artificial intelligence (AI) techniques have been integrated into measurement practices to handle massive computational loads, automate repetitive procedures, and optimize decision-making based on psychometric evidence. The emerging applications of AI may inspire positive shifts in measurement norms. This study suggests a shift towards focusing on the format of developed measures or measurement products. Traditionally, measurement tools comprise a set of items selected by test developers based on previous studies, intended for uniform use unless rigorous revalidation is performed for scale modifications. This research highlights the challenges of using conventional uniform measures and proposes using pre-uniform measures with AI-derived applications to create optimized measurement solutions tailored to individual needs. It presents three illustrative examples involving various psychological constructs (i.e., creative activity engagement, exposure to racism, and openness personality trait) in different measurement contexts. Each example demonstrates how to apply a pre-uniform measure with an AI-derived method-such as metaheuristic algorithms, machine learning regularization, or large language models-to address a specific measurement objective or need. Furthermore, this work summarized a four-step implementation framework and discussed practical implications and future directions for advancing pre-uniform measures using AI applications.

关键词： Artificial intelligence Pre-uniform measures Machine learning metaheuristics algorithms Large language models Measurement solution

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A comprehensive review of microgrid control methods: Focus on AI, optimization, and predictive techniques

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Computers and Electrical Engineering 2025年 125卷

作者： Limouni, Tariq Yaagoubi, Reda Bouziane, Khalid Guissi, Khalid Baali, El Houssain Energy and Agro-equipment Department Hassan II Institute of Agronomy and Veterinary Rabat 10112 Morocco School of Geomatics and Surveying Engineering Hassan II Institute of Agriculture and Veterinary Medicine Rabat 10112 Morocco LERMA Higher School of Energy Engineering International University of Rabat Campus de l'UIR Parc Technopolis Rocade de Rabat-Salé Sala Al Jadida 11100 Morocco

With the depletion of fossil fuels, the integration of renewable energy sources as distributed energy resources has become mandatory. However, the uncertainty and intermittent nature of these sources introduce significant challenges to their integration into microgrids. Effective control systems are essential for ensuring smooth integration, managing energy storage systems, and maintaining microgrid safety. In this study, a review of recent control methods applied in microgrid management was conducted with a focus on AI, optimization, and predictive techniques. These advanced and intelligent control methods were chosen for their potential to address current challenges. This study examined the benefits, limitations, and areas for future improvement. In addition, it explores the potential and the challenges of hybrid control techniques, which are less discussed in the literature, to further enhance control system efficiency and performance. © 2025 Elsevier Ltd

关键词： Artificial intelligence Fault detection Hybrid control technique metaheuristics algorithms Microgrid control Model predictive control Optimization algorithms Power and energy management

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metaheuristics-Based Uplink Power Control Scheme for User-Centric Cell-Free Massive MIMO Systems

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IEEE ACCESS 2024年 12卷 96603-96616页

作者： Luyen, Tong Van Cuong, Nguyen Van Hanoi Univ Ind Fac Elect Engn Hanoi 100000 Vietnam

In the rapidly evolving landscape of Beyond 5G (B5G) networks, Cell-Free Massive Multiple-Input Multiple-Output (CFmMIMO) systems have emerged as a pivotal solution to address the escalating demands for seamless connectivity and low-latency communication. However, the effective management of radio resources, particularly in the uplink direction, presents formidable challenges in optimizing system performance while ensuring equitable treatment for all users. Existing uplink power control methods often grapple with the intrinsic complexity of the optimization problems they encounter. To mitigate these challenges, this paper introduces a novel approach termed the metaheuristics-based uplink power control scheme tailored explicitly for user-centric CFmMIMO systems. Leveraging advanced metaheuristic optimization techniques, our scheme navigates the intricacies of uplink power control by prioritizing three primary objectives: maximizing the minimum user Spectral Efficiency (SE), maximizing the sum SE, and striking a balance between these two objectives. Through rigorous exploration of potential solutions, our proposed scheme transcends the limitations of conventional methods, offering near-optimal solutions for uplink power control. Numerical simulations corroborate the efficacy of our approach, showcasing enhanced fairness among users and substantial gains in SE compared to traditional methods. This research represents a significant advancement in the practical implementation of user-centric CFmMIMO systems within B5G networks, presenting promising solutions to address the evolving requirements of future wireless communication.

关键词： Power control Uplink Resource management metaheuristics Programming Interference Wireless communication User centered design User experience User-centric cell-free massive MIMO uplink power control max-min fairness optimization sum SE optimization joint max-min fairness optimization and sum SE maximization multi-objective optimization metaheuristics algorithms

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Improving Human Brucellosis Susceptibility Mapping Using Effective and Simultaneously Metaheuristic-based Feature Selection and Hyperparameter Tuning

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Acta tropica 2025年 267卷 107657页

作者： Iman Zandi Ali Jafari Ali Asghar Alesheikh Department of GIS School of Surveying and Geospatial Engineering College of Engineering University of Tehran Tehran Iran. Electronic address: imanzandi.dgh@ut.ac.ir. Department of GIS Faculty of Geodesy and Geomatics Engineering K. N. Toosi University of Technology Tehran Iran. Electronic address: a.jafari2@email.kntu.ac.ir. Department of GIS Faculty of Geodesy and Geomatics Engineering K. N. Toosi University of Technology Tehran Iran Geospatial Big Data Computations and Internet of Things (IoT) Lab K. N. Toosi University of Technology Tehran Iran. Electronic address: alesheikh@kntu.ac.ir.

Human Brucellosis, a neglected zoonotic disease, affects 1.6 to 2.1 million people globally each year. In Iran, it has become a significant health concern, with an average annual incidence rate of 19.91 cases per 100,000 people. This study aims to create a reliable Human Brucellosis Susceptibility Map (HBSM) for Mazandaran Province using a hybrid machine learning approach that enhances performance through metaheuristic algorithms for feature and hyperparameter optimization. A transformation function is integrated into these algorithms to reduce computational and time complexities while simultaneously executing feature selection and hyperparameter tuning. Additionally, a two-phase mutation operator is employed to improve the performance of feature selection. The results indicate that the hybrid model of Support Vector Regression-Transformation Mutation Grey Wolf Optimizer (SVR-TMGWO) outperformed other models, achieving RMSE=0.7723, MAE=0.614, MdAE=0.473, and R=0.536. The predicted HBSM for 2018 identified 68 rural districts in Mazandaran Province as being in the High and Very High susceptibility classes. The susceptibility map can help decision-makers more effectively prevent, control, and manage Human Brucellosis in Mazandaran Province.

关键词： Feature selection Human brucellosis susceptibility mapping Hyperparameter tuning metaheuristics algorithms Support vector regression

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