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  1. 1441

    Predicting bearing capacity of gently inclined bauxite pillar based on numerical simulation and machine learning by Deyu WANG, Defu ZHU, Biaobiao YU, Chen WANG

    Published 2025-03-01
    “…Additionally, two optimization algorithms, Genetic Programming (GP) and Improved Quantum Particle Swarm Algorithm (IQPSO), were used to enhance model performance and establish a non-linear mapping relationship between the influencing factors and the strength of the gently inclined pillars. …”
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    Article
  2. 1442
  3. 1443

    Tactical Coordination-Based Decision Making for Unmanned Combat Aerial Vehicles Maneuvering in Within-Visual-Range Air Combat by Yidong Liu, Dali Ding, Mulai Tan, Yuequn Luo, Ning Li, Huan Zhou

    Published 2025-02-01
    “…The coordinated situation assessment results are used to match corresponding tactics and maneuver control quantities. Finally, an improved particle swarm optimization algorithm (I-PSO) is proposed, which enhances the optimization ability and real-time performance through the design of local social factor iterative components and adaptive adjustment of inertia weights. …”
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    Article
  4. 1444

    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
    “…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
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  10. 1450

    Similar Instances Reuse Based Numerical Control Process Decision Method for Prismatic Parts by Changhong XU, Shusheng ZHANG, Jiachen LIANG, Rui HUANG, Rong BIAN

    Published 2025-01-01
    “…In addition, the existing NC process decision methods, such as the genetic algorithm, ant colony algorithm, and particle swarm algorithm, have not been combined with the reuse approach. …”
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    Article
  11. 1451

    Fault recovery strategy for urban distribution networks using soft open points by Liangsheng Lan, Guangqi Liu, Sitong Zhu, Min Hou, Xinrui Liu

    Published 2024-02-01
    “…By employing a binary particle swarm optimisation algorithm, this method effectively improves load recovery, reduces the number of switching operations, and minimises network loss. …”
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    Article
  12. 1452

    Research on the Operation Optimization of Public Building Systems in Extremely Cold Areas Based on Flexible Loads by Chuan Tian, Shunli Jiang, Shuai Li, Guohui Feng, Bin Yu

    Published 2024-11-01
    “…A two-stage operation optimization method is proposed: the first stage simulates the starting and stopping control conditions of equipment at varying temperatures and times, selecting the optimal time period to regulate the thermal loads; the second stage employs a multi-objective particle swarm optimization algorithm to optimize the scheduling of the system’s electrical load. …”
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    Article
  13. 1453

    Multi-Objective Parameter Optimization of Electro-Hydraulic Energy-Regenerative Suspension Systems for Urban Buses by Zhilin Jin, Xinyu Li, Shilong Cao

    Published 2025-06-01
    “…To streamline multi-objective optimization processes, a particle swarm optimization–back propagation (PSO-BP) neural network surrogate model was developed to approximate the complex co-simulation system. …”
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    Article
  14. 1454

    Dynamic strategy for adaptive block size optimization in blockchain technology by Shafique Ahmed Awan, Muazzam Ali Khan Khattak, Anwar Ali Sathio, Haleem Farman, Sumaira Memon, Moustafa M. Nasralla, Ahmed Sedik

    Published 2025-08-01
    “…This study proposes a dynamic block size optimization framework for private blockchain networks using hybrid heuristic algorithms—Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO)—integrated with Merkle trees and Directed Acyclic Graphs (DAGs). …”
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    Article
  15. 1455

    Hardware-in-loop implementation of an adaptive MPPT controlled PV-assisted EV charging system with vehicle-to-grid integration by Surabhi Singh, Hari Om Bansal

    Published 2025-08-01
    “…This paper developed and compared perturb and observe (P&O), Particle swarm optimization (PSO), and hybrid PSO + Adaptive neuro-fuzzy inference system (ANFIS) based algorithm for MPPT. …”
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    Article
  16. 1456

    Modified invasive weed optimization MPPT approach for PV system interfaced with BLDC motor for water pumping system by S. Nagaraja Rao, B. M. Kiran Kumar, B. M. Manjunatha, A. Suresh Kumar

    Published 2025-06-01
    “…Therefore, the modified invasive weed optimization (MIWO) approach integrated with P&O approach to improve performance under PSC. Results of proposed MIWO with P&O approach has been compared with other MPP approaches i.e., grey wolf optimization (GWO) approach, particle swarm optimization (PSO) approach, and genetic algorithm (GA) approaches for MPP tracking under different PSCs. …”
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    Article
  17. 1457

    A predictive framework using advanced machine learning approaches for measuring and analyzing the impact of synthetic agrochemicals on human health by Sahezpreet Singh, Puneet Kaur, Inderdeep Kaur, Gurpreet Singh, Satinder Kaur, Parminder Kaur

    Published 2025-05-01
    “…A custom loss function is leveraged to accurately predict the mortality cases and avoid misclassifications by penalizing the false negatives. Furthermore, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) used for model optimization. …”
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    Article
  18. 1458
  19. 1459

    Intelligent optimization method of fracturing parameters for shale oil reservoirs in Jimsar Sag, Junggar Basin, NW China by Yunjin WANG, Fujian ZHOU, Hang SU, Leyi ZHENG, Minghui LI, Fuwei YU, Yuan LI, Tianbo LIANG

    Published 2025-06-01
    “…A policy gradient-genetic-particle swarm algorithm is designed, which can adaptively adjust the inertia weights and learning factors in the iterative process, significantly improving the optimization ability of the optimization strategy. …”
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    Article
  20. 1460

    Determination of Sequential Well Placements Using a Multi-Modal Convolutional Neural Network Integrated with Evolutionary Optimization by Seoyoon Kwon, Minsoo Ji, Min Kim, Juliana Y. Leung, Baehyun Min

    Published 2024-12-01
    “…This study proposes a hybrid workflow for determining the locations of production wells during primary oil recovery using a multi-modal convolutional neural network (M-CNN) integrated with an evolutionary optimization algorithm. The particle swarm optimization algorithm provides the M-CNN with full-physics reservoir simulation results as learning data correlating an arbitrary well location and its cumulative oil production. …”
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    Article