Accurate Localization Method Combining Optimized Hybrid Neural Networks for Geomagnetic Localization with Multi-Feature Dead Reckoning by Suqing Yan, Baihui Luo, Xiyan Sun, Jianming Xiao, Yuanfa Ji, Kamarul Hawari bin Ghazali

“…To address these issues, we propose a fusion localization algorithm based on particle swarm optimization. First, we construct a five-dimensional hybrid LSTM (5DHLSTM) neural network model, and the 5DHLSTM network structure parameters are optimized via particle swarm optimization (PSO) to achieve geomagnetic localization. …”
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ICSO: A Novel Hybrid Evolutionary Approach with Crisscross and Perturbation Mechanisms for Optimizing Generative Adversarial Network Latent Space by Zhihui Chen, Ting Lan, Zhanchuan Cai, Zonglin Liu, Renzhang Chen

“…This paper proposes a novel improved crisscross optimization (ICSO) algorithm, a hybrid evolutionary approach that integrates crisscross optimization and perturbation mechanisms to find the suitable latent vector. …”
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Hybrid Types of Waves Propagating in Double Ridged Waveguide with Piecewise-Layer Dielectric Filling by A. V. Donchenko, G. F. Zargano, V. V. Zemlyakov

“…The calculations are carried out using the method of partial regions. The presented algorithm takes into account the electromagnetic field components singularities near the dielectric and metal edges of the waveguide. …”
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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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Security state estimation based on signal reconstruction for multi‐vehicle systems under malicious attack by Jing Wang, Siyuan Wang, Shan Lu, Meng Zhou

“…First the reconstruction of attack signal is transformed into a sparse error correction problem by stacking the measurement information of adjacent vehicles, and is solved by orthogonal matching pursuit (OMP) algorithm. Then the attack compensation based particle filter is designed to estimate the target state for each vehicle. …”
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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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A throughput and priority optimization strategy for high density healthcare IoT by Zhenlang Su, Junyu Ren, Yeheng Huang, Yang Liao, Tuanfa Qin

“…Firstly, the complex interference problem among WBANs is converted into a distance-based graph coloring model, then time division multiple access and a two-level split clustering methods are adopted to allocate initial time slots for nodes. Secondly, the particle swarm optimization algorithm is used to optimize the time slot of each node for maximizing the throughput. …”
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Parameter Design and Performance Optimization of Aerostatic Bearing by YANG Chunmei, CAO Bingzhang

“…In this paper, problems such as loadcarrying capacity, low stiffness, and vibration caused by large volume flow rate of aerostatic bearing has been studied In order to solve these problems, the particle swarm optimization algorithm is used to optimize the key parameters of throttle orifice on aerostatic bearing The simplified twodimensional Reynolds equation is solved by finite element method and the mathematical model is built Based on this model, the main performance parameters such as loadcarrying capacity, stiffness and volume flow rate of aerostatic bearing are calculated The coupling relationship between the structural dimension parameters which determining the main performance of aerostatic bearing is analyzed The multiobjective optimization design of the structural dimension parameters is carried out by particle swarm optimization With the simulation calculation of the aerostatic bearing, the loadcarrying capacity, stiffness, volume flow rate and other relevant performances are calculated The main performance of the optimized aerostatic bearing is compared with the original data The results show that compared with the performance before optimization, the loadcarrying capacity, stiffness of the aerostatic bearing are increased by 17%, 363% and the volume flow rate is decreased by 434% And the problems such as low loadcarrying capacity, low stiffness, and vibration caused by large volume flow rate of aerostatic bearing has been solved…”
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Machine Learning Classification of 3D Intracellular Trafficking Using Custom and Imaris-Derived Motion Features by Oleg Kovtun

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Battle Royale Optimization for Optimal Band Selection in Predicting Soil Nutrients Using Visible and Near-Infrared Reflectance Spectroscopy and PLSR Algorithm by Jagadeeswaran Ramasamy, Anand Raju, Kavitha Krishnasamy Ranganathan, Muthumanickam Dhanaraju, Backiyathu Saliha, Kumaraperumal Ramalingam, Sathishkumar Samiappan

“…Further partial least square regression (PLSR) was used to find the latent variable and to evaluate various algorithms for their performance in predicting soil properties. …”
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A Path-Driven Fluid Routing and Scheduling Method for Continuous-Flow Microfluidic Biochips with Delay Time Optimization by Zhisheng Chen, Bowen Liu, Hongjin Su, Zhen Chen, Genggeng Liu, Xing Huang

“…For routing, we develop a hybrid particle swarm optimization algorithm that incorporates conflict awareness and channel utilization strategies. …”
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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

“…To optimize the model’s parameters, we leverage the particle swarm optimization algorithm. The robustness of our proposed model is validated through extensive experiments performed on a financial dataset of Chinese listed companies. …”
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Parameter Optimization Design of Light induction Power-taking Device for Transmission Cable by LIN Jin-shu, ZHOU Mao-xi, CHEN Dao-mo, WANG Lu-yang, SUI Li-cheng

“…Increased stress and longterm operation will endanger the safety of the power system Therefore, based on the premise of reducing the weight of the magnetic core, this paper adopts the particle swarm algorithm to globally optimize the magnetic core size, air gap size and the number of turns of the coil. …”
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Flow Control of Tractor Multi-Channel Hydraulic Tester Based on AMESim and PSO-Optimized Fuzzy-PID by Qinglun Li, Xuefeng Bai, Yang Lu, Xiaoting Deng, Zhixiong Lu

“…Through MATLAB/Simulink (R2022a) simulations, the PSO algorithm optimizes the fuzzy membership functions and PID gains, yielding faster response, reduced overshoot, and minimal steady-state error. …”
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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. The inner layer aims at the optimal effect of peak shaving and valley filling. …”
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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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Multi-objective operation optimization method of microgrid considering the influence of electric vehicle by Tiefeng Xu, Xiaofang Meng, Fangfang Zheng, Yiduo Zhang, Xin Wu, Mingyang Li

“…Taking the minimum total operating cost and the minimum peak-valley difference of the microgrid in one day as the optimization objective, and considering many constraints such as power balance constraints and output constraints of distributed generation units, the multi-objective optimization function is transformed into a single-objective optimization function by linear weighting method, and the model is solved by particle swarm optimization algorithm. Finally, taking the typical daily load data of a micro-grid in a certain area as an example, the comparative results of economic cost and load curve after three scenarios optimization, namely, no EV access, EV access disorderly charging and discharging, are obtained respectively. …”
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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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Viewpoint Selection for 3D Scenes in Map Narratives by Shichuan Liu, Yong Wang, Qing Tang, Yaoyao Han

“…Pearson’s correlation coefficient is used to evaluate the relationship between visual salience and narrative relevance, serving as a constraint to construct a viewpoint fitness function that integrates the visual salience of the convex polyhedron enclosing the scene. The chaotic particle swarm optimization (CPSO) algorithm is utilized to locate the viewpoint position while maximizing the fitness function, identifying a viewpoint meeting narrative and visual salience requirements. …”
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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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