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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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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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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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Using Multi-objective Optimization and Finite Element Method to Reduce Cogging Torque in a Brushless DC Motor

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IETE JOURNAL OF RESEARCH 2024年第7期70卷 6350-6360页

作者： Salarian, Mina Azari, Milad Niaz Hajiaghaei- Keshteli, Mostafa Univ Sci & Technol Mazandaran Dept Elect Engn Behshahr Iran Univ Sci & Technol Mazandaran Dept Ind Engn Behshahr Iran

A significant issue in the design of the Brushless Direct Current (BLDC) motors is the cogging torque reduction that leads to negative effects on the BLDC motor's performance such as vibration and noise. However, most methods proposed to reduce cogging torque influence the output torque. Hence, this research aims to reduce cogging torque without having a significant effect on the output torque. For achieving the desired aim which is minimizing the cogging torque concerning the value of output torque, multi-objective optimization is a reliable approach. In this paper, some well-known multi-objective optimization including Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Simulated Annealing (MOSA), and Multi-Objective Red Deer Algorithm (MORDA), are employed to obtain the optimal design of a BLDC motor. In all used optimization algorithms, Simulation results are satisfying and display a significant reduction in the cogging torque, as well as the output torque increases.

关键词： Brushless DC (BLDC) motors Cogging torque metaheuristics algorithms Optimization Output torque Reduction

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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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A novel reward-based golden jackal optimization algorithm uses mix-weighted dynamic and random opposition learning to solve optimization problems

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第5期28卷 1-57页

作者： Mohapatra, Sarada Sarangi, Priteesha Mohapatra, Prabhujit Vellore Inst Technol Dept Math Vellore 632014 Tamil Nadu India

The application of meta-heuristic algorithms has significantly increased in recent years to find optimal solutions for continuous optimization problems. The Golden Jackal Optimizer (GJO) is a recently proposed swarm-based algorithm that has been considered to be a promising model of a meta-heuristic. Despite its superior performance, the GJO algorithm has flaws, including getting stuck in the local optimal regions and a lack of exploration ability, resulting in premature convergence when dealing with highly challenging optimization problems. Therefore, to circumvent this drawback, this paper proposes a Q-learning strategy combined with a novel adaptive mix-weighted dynamic opposition-based strategy (AMD) and random opposition-based learning (ROBL) strategy named the AMDRO-GJO algorithm. At first, the Q-learning method establishes a switching mechanism between AMD and ROBL strategies for the algorithm's exploration. Lastly, during the updating phase, AMDRO-GJO identifies the best scheme for the global best solution, enhancing the algorithm's exploitation. The effort of the proposed AMDRO-GJO algorithm has been examined on 23 classical, CEC2017, and CEC2019 benchmark functions. In addition, non-parametric tests such as the Wilcoxon rank sum test and t-test have been carried out to check the significance difference of the algorithms. Furthermore, the efficiency of several real-world engineering challenges has been evaluated by comparing it with those of rival optimizers. These experimental outcomes reveal the proposed AMDRO-GJO algorithm's outstanding performance in tackling multiple optimization problems.

关键词： metaheuristics algorithms Q-learning algorithm Dynamic opposition-based learning Random opposition-based learning Engineering problems

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Prediction of lake water-level fluctuations using adaptive neuro-fuzzy inference system hybridized with metaheuristic optimization algorithms

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APPLIED WATER SCIENCE 2023年第1期13卷 1-17页

作者： Quoc Bao Pham Mohammadi, Babak Moazenzadeh, Roozbeh Heddam, Salim Zola, Ramiro Pillco Sankaran, Adarsh Gupta, Vivek Elkhrachy, Ismail Khedher, Khaled Mohamed Duong Tran Anh Thu Dau Mot Univ Inst Appl Technol Thu Dau Mot Binh Duong Prov Vietnam Lund Univ Dept Phys Geog & Ecosyst Sci Solvegatan 12 SE-22362 Lund Sweden Shahrood Univ Technol Fac Agr Dept Water Engn Shahrood Iran Univ 20 Aout 1955 Fac Sci Agron Dept Hydraul DivLab Res Biodivers Interact Ecosyst & Route El HadaikBP 26 Skikda Algeria Univ Mayor San Andres Inst Hidraul & Hidrol La Paz Bolivia TKM Coll Engn Kollam Dept Civil Engn Kollam 691005 India Indian Inst Technol Roorkee Dept Hydrol Roorkee Uttar Pradesh India Najran Univ Coll Engn Civil Engn Dept Najran Saudi Arabia King Khalid Univ Coll Engn Dept Civil Engn Abha 61421 Saudi Arabia High Inst Technol Studies Dept Civil Engn Mrezgua Univ Campus Nabeul 8000 Tunisia Van Lang Univ Inst Computat Sci & Artificial Intelligence Lab Environm Sci & Climate Change Ho Chi Minh City Vietnam Van Lang Univ Fac Environm Ho Chi Minh City Vietnam

Lakes help increase the sustainability of the natural environment and decrease food chain risk, agriculture, ecosystem services, and leisure recreational activities locally and globally. Reliable simulation of monthly lake water levels is still an ongoing demand for multiple environmental and hydro-informatics engineering applications. The current research aims to utilize newly developed hybrid data-intelligence models based on the ensemble adaptive neuro-fuzzy inference system (ANFIS) coupled with metaheuristics algorithms for lake water-level simulation by considering the effect of seasonality on Titicaca Lake water-level fluctuations. The classical ANFIS model was trained using three metaheuristics nature-inspired optimization algorithms, including the genetic algorithm (ANFIS-GA), particle swarm optimizer (ANFIS-PSO), and whale optimization algorithm (ANFIS-WOA). For determining the best set of the input variables, an evolutionary approach based on several lag months has been utilized prior to the lake water-level simulation process using the hybrid models. The proposed hybrid models were investigated for accurately simulating the monthly water levels at Titicaca Lake. The ANFIS-WOA model exhibited the best prediction performance for lake water-level pattern measurement in this study. For the best scenario (the inputs were Xt-1, Xt-2, Xt-3, Xt-4, Xt-12) the ANFIS-WOA model attained root mean square error (RMSE approximate to 0.08 m), mean absolute error (MAE approximate to 0.06 m), and coefficient of determination (R-2 approximate to 0.96). Also, the results showed that long-term seasonal memory for this lake is suitable input for lake water- level models so that the long-term dynamic memory of 1-year time series for lake water-level data is the best input for estimating the water level of Titicaca Lake.

关键词： metaheuristics algorithms Lake water-level prediction Surface water Hybrid model South America Freshwater management

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A new hybrid optimization technique based on antlion and grasshopper optimization algorithms

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EVOLUTIONARY INTELLIGENCE 2023年第4期16卷 1383-1422页

作者： Amaireh, Anas Atef Al-Zoubi, Asem S. Dib, Nihad, I Univ Oklahoma Sch Elect & Comp Engn Norman OK 73071 USA Yarmouk Univ Telecommun Engn Dept POB 35 Irbid 21163 Jordan Jordan Univ Sci & Technol Elect Engn Dept POB 3030 Irbid 22110 Jordan

This paper proposes a new hybrid algorithm that merges the main features of two well-known metaheuristic algorithms;Grasshopper Optimization Algorithm (GOA) and Antlion Optimization (ALO) algorithm. ALO is strong in exploitation due to the mechanism of antlions in hunting other insects. On the other hand, the social forces in GOA represent the strong capability of exploration all over the search space. So, these features give the chance to combine ALO and GOA in one hybrid algorithm that significantly enhances the performance of both methods. The proposed hybrid algorithm is tested on 32 well-known benchmark test functions, 13 functions of the challenging CEC2015 functions, and two real problems in antenna array synthesis where the elements' excitation amplitudes and phases are optimized to minimize the maximum sidelobe level and impose nulls at specific angles. Comparisons show that the proposed algorithm outperforms 18 well-known optimization methods, including ALO and GOA, in the majority of these tests, with huge differences in some of them, which prove the stability, robustness, and efficiency of the proposed method over other robust algorithms.

关键词： Optimization metaheuristics algorithms Evolutionary algorithms Benchmark algorithm Antlion optimization algorithm Grasshopper optimization algorithm

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