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  1. 1861

    Behavior Analysis of the New PSO-CGSA Algorithm in Solving the Combined Economic Emission Dispatch Using Non-parametric Tests by Milena Gajić, Sanela Arsić, Jordan Radosavljević, Miroljub Jevtić, Bojan Perović, Dardan Klimenta, Miloš Milovanović

    Published 2024-12-01
    “…This paper proposes a new metahaeuristic algorithm named particle swarm optimization and chaotic gravitational search algorithm (PSO-CGSA) for solving the combined economic and emission dispatch (CEED) problem. …”
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    Article
  2. 1862

    An improved synergistic dual-layer feature selection algorithm with two type classifier for efficient intrusion detection in IoT environment by G Logeswari, K Thangaramya, M Selvi, J. Deepika Roselind

    Published 2025-03-01
    “…A robust feature selection subsystem, employing Synergistic Dual-Layer Feature Selection (SDFC) algorithm, combines statistical methods, such as mutual information and variance thresholding, with advanced model-based techniques, including Support Vector Machine (SVM) with Recursive Feature Elimination (RFE) and Particle Swarm Optimization (PSO) are employed to identify the most relevant features. …”
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    Article
  3. 1863

    Optimization Research on Energy Management Strategies and Powertrain Parameters for Plug-In Hybrid Electric Buses by Lufeng Wang, Juanying Zhou, Jianyou Zhao

    Published 2024-11-01
    “…Subsequent to this, a combined multi-layer powertrain optimization method based on Genetic Algorithm-Optimal Adaptive Control of Motor Efficiency-Particle Swarm Optimization (GOP) is proposed. …”
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    Article
  4. 1864

    Carbon Quota Allocation Prediction for Power Grids Using PSO-Optimized Neural Networks by Yixin Xu, Yanli Sun, Yina Teng, Shanglai Liu, Shiyu Ji, Zhen Zou, Yang Yu

    Published 2024-12-01
    “…The proposed model employs a hybrid of the gray forecasting model-particle swarm optimization-enhanced back-propagation neural network (GM-PSO-BPNN) for forecasting and allocating the total carbon quota. …”
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    Article
  5. 1865

    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

    Published 2025-03-01
    “…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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    Article
  6. 1866

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

    Published 2021-01-01
    “…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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    Article
  7. 1867

    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

    Published 2025-01-01
    “…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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    Article
  8. 1868

    Source-storage-load optimization control technology of DC microgrid with virtual energy storage by WANG Chang, JIANG Yu, FU Shouqiang, SHU Yinan, ZHANG Xiangyu

    Published 2025-04-01
    “…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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    Article
  9. 1869

    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

    Published 2024-01-01
    “…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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    Article
  10. 1870

    CNN-LSTM-Attention with PSO optimization for temperature and fault prediction in meat grinder motors by Yao Zhang, Pengfei Zhang, Wenchao Zhang, Mingwei Wang

    Published 2025-05-01
    “…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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    Article
  11. 1871

    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

    Published 2019-01-01
    “…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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    Article
  12. 1872

    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

    Published 2025-08-01
    “…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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    Article
  13. 1873
  14. 1874

    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

    Published 2025-06-01
    “…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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    Article
  15. 1875

    Applying an optimized low risk model for fast history matching in giant oil reservoir by Mojtaba karimi, Ali Mortazavi, Mohammad Ahmadi

    Published 2019-02-01
    “…Finally, the process was optimized by two main algorithms for reaching best solutions which are genetic and particle swarm optimization. …”
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    Article
  16. 1876

    Multiobjective Demand Double-Layer Energy Consumption Optimization Strategy for Microgrid Based on Improved HPSOFA by Bin Zhang, Jue Wang, Bo Li

    Published 2023-01-01
    “…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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    Article
  17. 1877

    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

    Published 2025-09-01
    “…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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    Article
  18. 1878

    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

    Published 2025-07-01
    “…Secondly, a particle swarm optimization algorithm was utilized for model solution optimization. …”
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    Article
  19. 1879
  20. 1880