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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Risk Prediction and Response Strategies in Corporate Financial Management Based on Optimized BP Neural Network by Meijia Zhai

“…To improve the accuracy of financial risk prediction, principal component analysis and particle swarm algorithm are applied to optimize the BP neural network model, the input data of the prediction model is improved, and the optimal initial weights and thresholds are given to the BP neural network by using particle swarm algorithm search, whereby the financial risk prediction model of particle swarm optimization BP neural network is constructed. …”
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An Optimized Cascaded CNN Approach for Feature Extraction From Brain MRIs for Tumor Classification by Santosh Kumar Chhotray, Debahuti Mishra, Sarada Prasanna Pati, Sashikala Mishra

“…The enhanced version of the Custom-CNN-CBAM model utilizing the BEO, referred to as Custom-CNN-CBAM-BEO, demonstrated superior convergence speed and accuracy compared to models optimized with genetic algorithm (GA) and particle swarm optimization (PSO), highlighting the effectiveness of the BEO optimization approach. …”
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Source-storage-load optimization control technology of DC microgrid with virtual energy storage by WANG Chang, JIANG Yu, FU Shouqiang, SHU Yinan, ZHANG Xiangyu

“…On this basis, under the coordinated control of source and storage of DC microgrid, particle swarm optimization algorithm is used to optimize the design of virtual capacitance values in different periods of time to improve the economic benefits of the system. …”
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Modeling and Optimization for Submersible Pump Tubular Linear Motor Based on Dynamic Load Analysis by Qiyi Wu, Bowen Xu, Xing Liu, Lin Qiu, Jien Ma, Caisong Yan, Xin Yin, Youtong Fang

“…Based on the proposed model, the impacts of the motor structural parameters, materials, and operating conditions on the electricity consumption per ton of fluid were analyzed. Furthermore, the particle swarm optimization algorithm was applied to the proposed model to optimize the SPPMTLM performance under different well fluid supply capacities. …”
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CNN-LSTM-Attention with PSO optimization for temperature and fault prediction in meat grinder motors by Yao Zhang, Pengfei Zhang, Wenchao Zhang, Mingwei Wang

“…In this paper, a deep learning model, CNN-LSTM-AP, is developed, combining convolutional neural network (CNN), long short-term memory network (LSTM), attention mechanism (Attention), and particle swarm optimization (PSO). The Attention mechanism is used to assign weights to input features, enhancing the model focus on important data. …”
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Distribution Network Reconfiguration Using Selective Firefly Algorithm and a Load Flow Analysis Criterion for Reducing the Search Space by Cassio Gerez, Lindenberg I. Silva, Edmarcio A. Belati, Alfeu J. Sguarezi Filho, Eduardo C. M. Costa

“…Results found for simulations with 33, 70, and 84 buses are presented and comparisons with selective particle swarm optimization (SPSO) and selective bat algorithm (SBAT) are made.…”
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Optimizing electric vehicle energy consumption prediction through machine learning and ensemble approaches by Izhar Hussain, Kok Boon Ching, Chessda Uttraphan, Kim Gaik Tay, Adeeb Noor, Sufyan Ali Memon

“…The K-Nearest Neighbors (KNN) algorithm is employed as the base model, with hyperparameter optimization performed using GridSearchCV, RandomizedSearchCV, Optuna, and Particle Swarm Optimization (PSO). …”
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A comprehensive review of AI and machine learning techniques in antenna design optimization and measurement by Pradnya A. Gajbhiye, Satya P. Singh, Madan Kumar Sharma

“…This review examines the latest advancements in applying AI/ML approaches to antenna design optimization. It explores the use of various AI/ML algorithms such as neural networks, decision trees, genetic algorithms, and particle swarm optimization in this context. …”
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Applying an optimized low risk model for fast history matching in giant oil reservoir by Mojtaba karimi, Ali Mortazavi, Mohammad Ahmadi

“…Finally, the process was optimized by two main algorithms for reaching best solutions which are genetic and particle swarm optimization. …”
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Multiobjective Demand Double-Layer Energy Consumption Optimization Strategy for Microgrid Based on Improved HPSOFA by Bin Zhang, Jue Wang, Bo Li

“…In order to optimize the economy and environmental protection of microgrid, this paper establishes a demand response model based on comprehensive satisfaction, combines the advantages of the classical multiobjective particle swarm algorithm and multiobjective firefly algorithm, and proposes a hybrid particle swarm optimization and firefly algorithm (HPSOFA) to solve the joint economic and environmental dispatch problem of microgrid and improve the wind and light consumption capacity. …”
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Reliability evaluation and multi-objective optimization of combustion chamber’s key components of marine engine by Lei Hu, Wentong Wang, Xu Wang, Jianguo Yang, Yonghua Yu, Chunyang Mei

“…The research shows that the multi-objective particle swarm optimization algorithm achieves the best performance, the maximum temperatures of the piston, cylinder head, and liner decrease by 3.90 %, 5.66 %, and 6.52 %, the maximum thermo-mechanical coupling stresses reduced by 9.41 %, 7.83 %, and 4.97 % respectively, and creep-fatigue life enhancements reach 3.84 % and 12.67 % for the piston and cylinder head. …”
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Research on optimization technology of new pipeline design for regional natural gas pipeline network by Jingyi CUI, Kunfeng ZHU, Cuixian GAO, Li GU, Jing REN, Yuxing LI, Wuchang WANG

“…Secondly, a particle swarm optimization algorithm was utilized for model solution optimization. …”
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Data Mining Techniques for Early Detection and Classification of Plant Diseases: An Optimization-Based Approach by Wagh Swapnil, Sharma Ruchi

“…Furthermore, low-level optimization techniques like genetic algorithms as well as particle swarm optimization are used to fine tune the specific model parameters and to reduce the computational overhead for improving the detection efficacy still more. …”
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An Investigation into the Rescue-Path Planning Algorithm for Multiple Mine Rescue Teams Based on FA-MDPSO and an Improved Force-Directed Layout by Qiangyu Zheng, Peijiang Ding, Zhixin Qin, Zhenguo Yan

“…Subsequently, the hyperparameters of MDPSO (Multiple Constraints Discrete Particle Swarm Optimisation) were optimised by means of four intelligent algorithms—ACO (Ant Colony Optimization), FA (Firefly Algorithm), GWO (Grey Wolf Optimizer) and WOA (Whale Optimization Algorithm). …”
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Tooth Profile Modification Method of RV Reducer Cycloid Gear Aiming at Optimizing Carrying Capacity by Zongwen An, Panlong Jia, Qiang Wang, Zhili Li, Mingyuan Lei

“…Cycloid gear tooth profile modification is very important to the performance of RV reducer，in order to select the shape modification method and parameter size reasonably and improve the stress state of the cycloid tooth surface，an equidistant & radical-moving modification method based on particle swarm optimization algorithm to optimize bearing capacity is proposed. …”
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Harris Hawks Optimization for Soil Water Content Estimation in Ground-Penetrating Radar Waveform Inversion by Hanqing Qiao, Minghe Zhang, Maksim Bano

“…To further validate our approach, the results of the HHO algorithm are also compared with those of partial swarm optimization (PSO) and grey wolf optimizer (GWO). …”
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Multi-layer Embedded Optimization of Microgrid Capacity Considering Price and Incentive/Compensation Coupling Mechanism by Xianhui ZHU, Xu HU, Nan SHI, Yao ZHANG, Jingwen ZHONG

“…Finally, the optimization models of both the source and load are coupled through the multi-layer embedded mechanism, and a solution model is constructed, which combines the multi-objective particle swarm optimization (MOPSO) algorithm and the particle swarm optimization–imperial competition algorithm (PSO-ICA). …”
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Design and parameter optimization of PBC-NDO composite controller for electric vehicle wireless charging system by YAN Rongge, YAN Chunjiao, YANG Qingxin, ZHANG Xian

“…Aiming at the problems that the charging voltage of the wireless charging system of electric vehicle is unstable due to the offset of the primary and secondary coils and load fluctuation in the process of variable voltage intermittent fast charging, and the controller parameters are mostly selected by empirical value and trial and error method, a composite control strategy combining passivity based controller (PBC) and nonlinear disturbance observer (NDO) based on particle swarm optimization algorithm is proposed. …”
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An integrated optimization model of network behavior victimization identification based on association rule feature extraction by Shengli ZHOU, Linqi RUAN, Rui XU, Xikang ZHANG, Quanzhe ZHAO, Yuanbo LIAN

“…The identification of the risk of network behavior victimization was of great significance for the prevention and warning of telecom network fraud.Insufficient mining of network behavior features and difficulty in determining relationships, an integrated optimization model for network behavior victimization identification based on association rule feature extraction was proposed.The interactive traffic data packets generated when users accessed websites were captured by the model, and the implicit and explicit behavior features in network traffic were extracted.Then, the association rules between features were mined, and the feature sequences were reconstructed using the FP-Growth algorithm.Finally, an analysis model of telecom network fraud victimization based on network traffic analysis was established, combined with the stochastic forest algorithm of particle swarm optimization.The experiments show that compared with general binary classification models, the proposed model has better precision and recall rates and can effectively improve the accuracy of network fraud victimization identification.…”
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