Analysis of MHD tangent hyperbolic hybrid nanofluid flow with different base fluids over a porous stretched sheet by Vishalkumar J. Prajapati, Ramakanta Meher

“…Finally, the impact of nano particle volume fractions of copper and aluminium oxide on the velocity and temperature profiles is discussed to study their efficiency.…”
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Optimizing sustainable blended concrete mixes using deep learning and multi-objective optimization by Rupesh Kumar Tipu, Preeti Rathi, Kartik S. Pandya, Vijay R. Panchal

“…The Multi-Objective Particle Swarm Optimization (MOPSO) algorithm finds multiple optimal solutions which simultaneously optimize three competing objectives that include strength maximization and cost minimization and cement reduction. …”
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Small-Signal Stability Analysis and Parameters Optimization of Virtual Synchronous Generator for Low-Inertia Power System by Alaa Altawallbeh, Abdulrahman Alassi, Nader Meskin, Mohammed A. Al-Hitmi, Ahmed M. Massoud

“…We further propose a hybrid Particle Swarm Optimization (PSO) algorithm with a multi-objective cost function to optimize VSG controller gains. …”
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Research on Sensitivity Improvement Methods for RTD Fluxgates Based on Feedback-Driven Stochastic Resonance with PSO by Rui Wang, Na Pang, Haibo Guo, Xu Hu, Guo Li, Fei Li

“…Simulink is used to construct the sensor model of odd polynomial feedback control, and the Particle Swarm Optimization (PSO) algorithm is used to optimize the coefficients of the feedback function so that the sensor reaches a resonance state, thus reducing the noise interference and improving the sensitivity of the sensor. …”
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Internal sound field reconstruction method based on optimal arrangement of sound monitoring points near the cabin inner surface by Shengguo Shi, Qiang Guo, Huihui He, Xinyu Wang

“…Besides, the optimum performance of different optimization algorithms is compared. The results show that the proposed method is more accurate than the traditional equivalent source method when the number of monitoring points is the same, and the QPSO algorithm shows the best monitoring point location optimization performance. …”
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Approximate Inertial Manifold-Based Model Reduction and Vibration Suppression for Rigid-Flexible Mechanical Arms by Lisha Xu, Hua Deng, Chong Lin, Yi Zhang

“…A limited number of sinusoidal signals approximately combine the input signal, by using the particle swarm optimization algorithm to optimize the input signal, and the amplitude of the sinusoidal signal is corrected. …”
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A strategic approach to the placement of PV-integrated EV charging stations for enhancing the distribution network performance by Raj Chakraborty, Subhojit Dawn, Priyanath Das, Diptanu Das, Sadhan Gope, Md. Minarul Islam, Taha Selim Ustun

“…The outcomes have been compared with Grey Wolf Optimizer, particle swarm optimization (PSO), and whale optimization algorithm (WOA) to validate the effectiveness of the optimal planning to allocate the EVCS and PV units. …”
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Enhancing Streamflow Prediction Accuracy: A Comprehensive Analysis of Hybrid Neural Network Models with Runge–Kutta with Aquila Optimizer by Rana Muhammad Adnan, Wang Mo, Ahmed A. Ewees, Salim Heddam, Ozgur Kisi, Mohammad Zounemat-Kermani

“…Abstract This study investigates the efficacy of hybrid artificial neural network (ANN) methods, incorporating metaheuristic algorithms such as particle swarm optimization (PSO), genetic algorithm (GA), gray wolf optimizer (GWO), Aquila optimizer (AO), Runge–Kutta (RUN), and the novel ANN-based Runge–Kutta with Aquila optimizer (LSTM-RUNAO). …”
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Optimizing Power System Performance: The Significance of Placement and Sizing of Battery Energy Storage Systems by Harith B. Hussien, Ahmed J. Abid, Naseer M. Yasin, Ameer L. Saleh, Hayder Jasim Habil

“…To do this, two meta-heuristic optimization algorithms are suggested in this paper, Particle Swarm Optimization (PSO) and Dragonfly Algorithm (DA). …”
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A Day-Ahead Economic Dispatch Method for Renewable Energy Systems Considering Flexibility Supply and Demand Balancing Capabilities by Zheng Yang, Wei Xiong, Pengyu Wang, Nuoqing Shen, Siyang Liao

“…The upper-layer model focuses on the economic efficiency of unit start-up and shut-down, utilizing a particle swarm algorithm to identify unit combinations that comply with minimum start-up and shut-down time constraints. …”
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Optimizing the core loading pattern and fuel composition in a hexagonal small modular nuclear reactor via ANN-PSO approach by G.R. Ansarifar, M.R. Karimi, H. Zayermohammadi Rishehri

“…The gadolinia concentration was effectively optimized using the Particle Swarm Optimization (PSO) algorithm. The best model, based on TCs and PPFs, was selected. …”
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Collaborative Planning for Distribution Network and PV-ESS in the Marketization Environment by Ping RAO, Yue XIANG, Haotian YAO, Youbo LIU, Junyong LIU

“…The double-layer improved particle swarm optimization algorithm was applied to run the model. …”
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Metaheuristic optimization of extreme gradient boosting machine for enhanced prediction of lateral strength of reinforced concrete columns under cyclic loadings by Phu-Anh-Huy Pham, Nhat-Duc Hoang

“…Since model selection is critical for constructing a reliable prediction mode, this study relies on utilizing metaheuristic approaches, including Genetic Algorithm, Particle Swarm Optimization, Artificial Bee Colony, and Ant Colony Optimization, to optimize the performance of the XGBoost model. …”
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Design, Modeling, and Optimization of a Nearly Constant Displacement Reducer with Completely Distributed Compliance by Yanchao Tong, Beibei Hou, Shuaishuai Lu, Pengbo Liu, Zhi Yang, Peng Yan

“…On the basis of sensitivity analysis to structure parameters, including node positions and beam parameters, the Particle Swarm Optimization (PSO) algorithm is used to optimize the displacement reduction performance. …”
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Study on dynamic resilient modulus prediction model of subgrade fine-grained soil based on physical property parameters by Fang Guo, Wei Gu

“…The relationship between k i parameters and key physical property parameters was acquired by utilizing artificial neural network and genetic algorithm. Finally, a prediction model considering physical property parameters was established, and the prediction results were compared with those of the general model in standard. …”
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Fluid flow characteristics estimation of a new integrated bifluid/airbased photovoltaic thermal system utilizing a hybrid optimization method by Ghassan A. Bilal, Abdullateef A. Jadallah, Omayma M. Abdulmajeed, Müslüm Arıcı

“…For the reliability of the proposed algorithm, the mean absolute percentage error was used for error estimation detection. …”
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Peak-Valley difference based pricing strategy and optimization for PV-storage electric vehicle charging stations through aggregators by Qin Yan, Jinxin Wang, Tao Lin, Archie James Johnston

“…Using Multi-Objective Particle Swarm Optimization (MOPSO), the strategy thereby incorporates three main objectives; minimizing grid load fluctuations, maximizing aggregator profits, and reducing user costs. …”
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Exploring alumina nanoparticle deposition in heat exchangers with hexagonal tubes: A hybrid approach integrating numerical simulations and machine learning by Seyed Hamed Godasiaei, Pouyan Talebizadehsardari, Amir Keshmiri

“…The model was developed using Python, integrating traditional Eulerian-Lagrangian approaches with advanced neural network algorithms, including Deep Neural Networks (DNN) and Long Short-Term Memory (LSTM) models. …”
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Early Remaining Useful Life Prediction for Lithium-Ion Batteries Using a Gaussian Process Regression Model Based on Degradation Pattern Recognition by Linlin Fu, Bo Jiang, Jiangong Zhu, Xuezhe Wei, Haifeng Dai

“…The model hyperparameters are further optimized through the particle swarm optimization (PSO) algorithm to improve the adaptability and generalization capability of the predictive models. …”
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Risk assessment of tunnel water inrush based on Delphi method and machine learning by Leizhi Dong, Qingsong Wang, Weiguo Zhang, Yongjun Zhang, Xiaoshuang Li, Fei Liu

“…Then, the Radial Basis Function (RBF) network, improved by the Locally Linear Embedding (LLE) algorithm and the Particle Swarm Optimization (PSO), is applied to predict the risk level. …”
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