Iterative Inversion of Normal and Lateral Resistivity Logs in Thin-Bedded Rock Formations of the Polish Carpathians by Michał Wilkosz

“…The proposed iterative inversion procedure combines a finite element method forward modeling procedure with a particle swarm optimization algorithm to generate high-resolution models of the rock formation. …”
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Intelligent Firefighting Technology for Drone Swarms with Multi-Sensor Integrated Path Planning: YOLOv8 Algorithm-Driven Fire Source Identification and Precision Deployment Strateg... by Bingxin Yu, Shengze Yu, Yuandi Zhao, Jin Wang, Ran Lai, Jisong Lv, Botao Zhou

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ROLLING BEARING WEAK FAULT FEATURE EXTRACTION METHOD WITH ALIF⁃NLM by WANG Ying, SONG YuBo, ZHU DaPeng

“…Aiming at the problem that the early weak fault feature was difficult to extract of rolling bearing under the strong noise background,combined with the advantages of adaptive local iterative filter(ALIF)and non⁃local means(NLM)method,an ALIF⁃NLM bearing weak fault feature extraction method was proposed.Firstly,a weighted kurtosis⁃energy ratio criterion was constructed to filter the intrinsic mode function(IMF)components of the ALIF decomposition and reconstruct the signal.Secondly,the minimum energy entropy⁃kurtosis ratio index was constructed by combining the sensitivity of kurtosis to the impact signal with the evaluation performance of energy entropy to the uniformity and complexity of signal energy distribution,and using this index as the fitness function,the adaptive selection of parameter combinations in NLM method was realized by particle swarm optimization(PSO)algorithm.Finally,the fault feature of the reconstructed signal was extracted with the adaptive NLM.The simulation and experimental results show that this method can effectively extract the weak fault feature information of rolling bearing under the strong noise background.…”
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Predicting financial distress in high-dimensional imbalanced datasets: a multi-heterogeneous self-paced ensemble learning framework by Ruize Gao, Shaoze Cui, Yu Wang, Wei Xu

“…Furthermore, the proposed framework incorporates the MHSPE model to iteratively identify the most informative majority class data samples, effectively addressing the class imbalance issue. To optimize the model’s parameters, we leverage the particle swarm optimization algorithm. …”
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Distributed energy storage configuration considering the vulnerability of active distribution network by ZHU Peixue, GUO Qian, LI Lingzhi, ZHENG Di, CAI Hui

“…In the outer layer, the optimal economic benefit is taken as the objective, the improvement degree of energy storage to the vulnerability of distribution network is considered in the constraint conditions, and the energy storage capacity is solved by particle swarm optimization algorithm. …”
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State of Charge Prediction of Mine-Used LiFePO₄ Battery Based on PSO-Catboost by Dazhong Wang, Yinghui Chang, Pengfei Ji, Yanchun Suo, Ning Chen

“…Firstly, the classification model based on Catboost is constructed, and then the particle swarm algorithm is used to optimize the Catboost hyperparameters to build the optimal model. …”
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Prediction of Total Organic Carbon Content in Shale Based on PCA-PSO-XGBoost by Yingjie Meng, Chengwu Xu, Tingting Li, Tianyong Liu, Lu Tang, Jinyou Zhang

“…In this study, for the shale of the Qingshankou Formation of the Gulong Sag in the Songliao Basin, TOC content prediction models using various machine learning algorithms are established and compared based on measured data, principal component analysis, and the particle swarm optimization algorithm. …”
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Improving with Hybrid Feature Selection in Software Defect Prediction by Muhammad Yoga Adha Pratama, Rudy Herteno, Mohammad Reza Faisal, Radityo Adi Nugroho, Friska Abadi

“…This research focuses on the problems that occur in Particle Swarm Optimization (PSO), such as the problem of noisy attributes, high-dimensional data, and premature convergence. …”
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Structural Parameter Identification Using Multi-Objective Modified Directional Bat Algorithm by LIU Li-jun, LIN Ying-hai, SU Yong-hui, LEI Ying

“…Compared to Multi-Objective Particle Swarm Optimization （MOPSO） and the Non-Dominated Sorting Genetic Algorithm-II （NSGA-II）， MOMDBA outperformed its counterpart. …”
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A hybrid framework for heart disease prediction using classical and quantum-inspired machine learning techniques by Ankur Kumar, Sanjay Dhanka, Abhinav Sharma, Rohit Bansal, Mochammad Fahlevi, Fazla Rabby, Mohammed Aljuaid

“…Subsequently, both classical and quantum-inspired models are trained and optimized. The classical models utilized Genetic Algorithms (CGA) and Particle Swarm Optimization (CPSO) for hyperparameter tuning, while the quantum-inspired models employed Quantum Genetic Algorithms (QGAs) and Quantum Particle Swarm Optimization (QPSO). …”
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Evaluation of the impact of the distance determination function on the results of optimization of the geographical placement of renewable energy sources-based generation using a... by Andrei M. Bramm, Stanislav A. Eroshenko

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An Improved Squirrel Search Algorithm for Optimization by Tongyi Zheng, Weili Luo

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An Optimization Method for Multi-Functional Radar Network Deployment in Complex Regions by Yi Han, Xueting Li, Xiangliang Xu, Zhenxing Zhang, Tianxian Zhang, Xiaobo Yang

“…This paper addresses the deployment of a multi-functional radar network (MFRN) in complex regions that may exhibit non-connectivity, holes, or concave shapes, utilizing multi-objective particle swarm optimization (MOPSO). Unlike traditional approaches that rely on constraint-handling techniques, the proposed methodology leverages the unique characteristics of polygonal deployment regions to enhance deployment efficiency. …”
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Three-Dimensional Drone Cell Placement: Drone Placement for Optimal Coverage by Aniket Basu, Hooman Oroojeni, Georgios Samakovitis, Mohammad Majid Al-Rifaie

“…The multi-drone-cell placement problem is solved using adapted Dispersive Flies Optimization alongside other meta-heuristic algorithms such as Particle Swarm Optimization and differential evolution. …”
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On the Use of an Improved Artificial Fish Swarm Algorithm-Backpropagation Neural Network for Predicting Dam Deformation Behavior by Bo Dai, Hao Gu, Yantao Zhu, Siyu Chen, E. Fernandez Rodriguez

“…The hybrid model’s preciseness is verified against the radial displacements of a pendulum in a concrete arch dam and simulations of four models: statistical model, BP-ANN optimized by genetic algorithm (GA), particle swarm optimization (PSO), and AFSA. …”
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State-of-charge estimation for Lithium-Ion batteries using Kalman filters based on fractional-order models by Likun Xing, Liuyi Ling, Bing Gong, Menglong Zhang

“…The Fractional-Order Model (FOM) parameters and differentiation orders are identified by the Particle Swarm Optimization (PSO) algorithm, and a pulsed-discharge test is implemented to verify the accuracy of parameter identification. …”
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Unscented transformation method for robustness verification of helicopter flight control system by LIU Changqi, LI Aijun, DUAN Guangzhan, LI Zuo

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Dynamic Inversion Method for Concrete Gravity Dam on Soft Rock Foundation by Guanglin Yin, Chaoning Lin, Taozhen Sheng, Wenbo Xue, Tongchun Li, Siyu Chen

“…To address this limitation, an improved particle swarm optimization (PSO) algorithm is developed for dynamic parameter inversion, combining real-time monitoring data with finite element modeling to evaluate the time-varying elastic modulus of the foundation. …”
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Task Allocation Method for Emergency Active Debris Removal Based on the Fast Elitist Non-Dominated Sorting Genetic Algorithm by Hao Lei, Xiang Zhang, Wenhe Liao, Guoning Wei, Shuhui Fan

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Flood-prone area mapping using a synergistic approach with swarm intelligence and gradient boosting algorithms by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Sani I. Abba, Jamil Hussain, Soo-Mi Choi

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