PSO-NMPC control strategy based path tracking control of mining LHD (scraper) by Ya Liu, Ping-an Peng, Li-guan Wang, Jia-xi Wu, Ming-yu Lei, Chao-wei Zhang, Ru Lei

“…To address these issues, this paper proposes a PSO-NMPC control strategy that integrates a particle swarm optimization algorithm (PSO) into the NMPC controller to enhance path tracking for LHDs. …”
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A Comparative Study of Customized Algorithms for Anomaly Detection in Industry-Specific Power Data by Minsung Jung, Hyeonseok Jang, Woohyeon Kwon, Jiyun Seo, Suna Park, Beomdo Park, Junseong Park, Donggeon Yu, Sangkeum Lee

“…This study compares and analyzes statistical, machine learning, and deep learning outlier-detection methods on real power-usage data from the metal, food, and chemical industries to propose the optimal model for improving energy-consumption efficiency. …”
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Research on Collaborative Optimization Strategy of Railway Signal Nonlinear Control System Based on BBO Algorithm and Multi-objective Optimization by Xue Li, Yixuan Yang, Zheng Li, Hui He

“…The research constructs a mathematical model with multiple objective constraints, accurately identifies the adaptation shortcomings of the existing system in dynamic scenarios, and then employs a Biogeography-Based Optimization (BBO) algorithm for global optimization of control parameters. …”
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Multi-objective optimization of grinding process parameters for complicated worm space surface based on the grey wolf optimization algorithm by Jiongkang Ren, Shisong Wang, Keqi Ren

“…To account for actual working conditions, we developed constrained models for grinding ratio and machining rigidity and improved the boundary processing method for MOGWO optimization. …”
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Fault Diagnosis Method for Transformer Winding Based on Differentiated M-Training Classification Optimized by White Shark Optimization Algorithm by Guochao Qian, Kun Yang, Jin Hu, Hongwen Liu, Shun He, Dexu Zou, Weiju Dai, Haozhou Wang, Dongyang Wang

“…The principal contributions are threefold: First, building upon the fundamental principles of the M-training algorithm, we establish a classification model incorporating diversified classifiers. …”
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The Optimal Location of Interline Power Flow Controller in the Transmission Lines for Reduction Losses using the Particle Swarm Optimization Algorithm by Mehrdad Ahmadi Kamarposhti

“…This paper proposed, the optimal location of the IPFC in electrical power systems, using the particle swarm optimization algorithm. …”
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An Optimization Method for Multi-Robot Automatic Welding Control Based on Particle Swarm Genetic Algorithm by Lu Chen, Jie Tan, Tianci Wu, Zengxin Tan, Guobo Yuan, Yuhao Yang, Chiang Liu, Haoyu Zhou, Weisi Xie, Yue Xiu, Gun Li

“…The method first constructs a multi-axis robotic kinematic model to provide constraint conditions. Then, the PSO (particle swarm optimization) algorithm, which integrates penalty functions into the fitness evaluation, is used to determine the optimal welding path by simulating collective behavior within a group. …”
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Multi-objective Optimization Design of Component Cooling System in HPR1000 by ZHAO Weiguang1, 2, YU Pei1, 3, ZENG Xiaobo1, 2, FAN Guangming1, 2, YAN Changqi1, 2

“…Each variable possesses a specific range of values, which is critical for the optimization process. The theoretical model and the final optimization results demonstrate that the proposed evaluation model and optimization algorithm effectively assess the RRI’s performance in multi-objective optimization calculations, allowing for a substantial degree of RRI optimization. …”
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Path Planning of UAV Using Levy Pelican Optimization Algorithm In Mountain Environment by Guiliang Zhou, Shuaiqi Lü, Lina Mao, Kaiwen Xu, Tianwen Bao, Xu Bao

“…The test results are compared and analyzed with those of classical, current mainstream, and recently released swarm intelligence optimization algorithms. Using 3D mountain range model experiments, the algorithm is compared before and after optimization, and the improved optimization algorithm is then applied to the actual problem of UAV 3D path planning. …”
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Optimal design of 10‐bit single‐slope ADC for CMOS image sensor based on swarm intelligent optimization algorithm by Yan‐Hua Ma, Ji‐Tong Li, Ming Zhu, Yu‐Chun Chang

“…Here, an optimal design methodology based on an improved artificial fish swarm optimization algorithm (IAFSOA) is proposed for a 10‐bit SS ADC in CIS. …”
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Estimation of Lithium-Ion Battery SOC Based on IFFRLS-IMMUKF by Xianguang Zhao, Tao Wang, Li Li, Yanqing Cheng

“…To tackle the challenge of precise SOC estimation in complex environments, this study introduces an improved forgetting factor recursive least squares (IFFRLS) method, which integrates the Golden Jackal optimization (GJO) algorithm with the traditional FFRLS method. …”
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Automated Generation of Hybrid Metaheuristics Using Learning-to-Rank by Xinru Xue, Ting Shu, Jinsong Xia

“…Metaheuristic algorithms, due to their superior global exploration capabilities and applicability, have emerged as critical tools for addressing complicated optimization tasks. …”
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FCC algorithm for power loss diminution by Kanagasabai L.

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SIMULATION AND OPTIMIZATION AS A TOOL FOR THE DEVELOPMENT OF HIGH EFFECTIVE TECHNOLOGICAL SCHEMES OF DISTILLATION by A. V. Timoshenko, E. A. Anokhina

“…This method has unfortunately been very energy intensive and requires a lot of heat. Mathematical modeling of the technological schemes of distillation is an effective method to improve existing and to create new industrial technologies. …”
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Application of the metaheuristic algorithms to quantify the GSI based on the RMR classification by Pouya Koureh Davoodi, Farnusch Hajizadeh, Mohammad Rezaei

“…With this objective, GSI quantification directly from RMR can be an attractive issue as it remains a complex task still due to the limited accuracy and generalizability of existing empirical models under varying geological conditions. This study addresses this challenge by analyzing data from fourteen different rock types and employing three metaheuristic optimization algorithms, namely Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Grey Wolf Optimization (GWO), to develop predictive models for quantifying GSI based on the RMR. …”
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LM-CNN-based Automatic Cost Calculation Model for Power Transmission and Transformation Projects by Xiaolin WU, Ling LUAN, Lianwu PAN, Hailong LI

“…Compared with the BP neural network and GD-CNN, the proposed model with higher prediction accuracy and stability combines the advantages of Levenberg-Marquart algorithm and convolutional neural network model to improve the calculation effect of power transmission and transformation project cost.…”
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Half-hourly electricity price prediction model with explainable-decomposition hybrid deep learning approach by Sujan Ghimire, Ravinesh C. Deo, Konstantin Hopf, Hangyue Liu, David Casillas-Pérez, Andreas Helwig, Salvin S. Prasad, Jorge Pérez-Aracil, Prabal Datta Barua, Sancho Salcedo-Sanz

“…Explainable Artificial Intelligence (xAI) methods were used to enhance model interpretability and trustworthiness, with optimization via the Optuna algorithm. …”
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Aerodynamic Shape Optimization of a Missile Using a Multiobjective Genetic Algorithm by Ahmet Şumnu, İbrahim Halil Güzelbey, Orkun Öğücü

“…The front part of the body, the main body, and tailfins were improved to find an optimum missile model at supersonic speeds. …”
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Optimizing Route for Hazardous Materials Logistics Based on Hybrid Ant Colony Algorithm by Haixing Wang, Guiping Xiao, Zhen Wei

“…To achieve the purpose of balancing risk and cost for route based on the principle of ACA that used to solve TSP, the improved HACA was designed. Considering the capacity of road network and the maximum expected risk limits, a route optimization model to minimize the total cost is established based on network flow theory. …”
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Predictive analytics of complex healthcare systems using deep learning based disease diagnosis model by Muhammad Kashif Saeed, Alanoud Al Mazroa, Bandar M. Alghamdi, Fouad Shoie Alallah, Abdulrhman Alshareef, Ahmed Mahmud

“…In addition, the convolutional neural network with long short-term memory (CNN-LSTM) approach is used to classify LCC. To optimize the hyperparameter values of the CNN-LSTM approach, the Chaotic Tunicate Swarm Algorithm (CTSA) approach was implemented to improve the accuracy of classifier results. …”
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