PID CONTROLLER FOR SPEED CONTROL OF PMSM BASED ON MAYFLY OPTIMIZATION ALGORITHM by Fatimah F. Jaber

“…This suggested approach has been verified with MATLAB, and the outcomes are compared with the standard particle swarm optimization technique (PSO) and conventional PID. …”
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Runoff Prediction and Uncertainty Analysis for Xijiang River Basin Based on CMIP6 Climate Scenarios by WU Huiming, YAN Meng, ZHOU Shuai

“…Based on this, the Xin'anjiang hydrological model (XAJ) is built, and the particle swarm optimization (PSO) algorithm is employed to calibrate and validate the model parameters. …”
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Joint Optimization of Berths and Quay Cranes Considering Carbon Emissions: A Case Study of a Container Terminal in China by Houjun Lu, Xiao Lu

“…This study presents an integrated model that incorporates tidal factors into the joint optimization of berth and quay crane operations, addressing both service standards and emissions during port stays and crane activities, and further designs a PSO-GA hybrid algorithm, combining particle swarm optimization (PSO) with crossover and mutation operators from a genetic algorithm (GA), to enhance optimization accuracy and efficiency. …”
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Multiobjective Optimization Design and Experimental Study of Desulfurization Dust Removal Centrifugal Pump Based on Immune Particle Swarm Algorithm by Yuqin Wang, Xinwang Huo, Hailong Ji

“…The immune particle swarm optimization algorithm was used to optimize the multiobjective function, and the optimal combination of the main parameters was obtained. …”
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Multipoint Optimal Minimum Entropy Deconvolution Adjusted for Automatic Fault Diagnosis of Hoist Bearing by Tengyu Li, Ziming Kou, Juan Wu, Waled Yahya, Francesco Villecco

“…In the second part, the particle swarm optimization (PSO) taking fractal dimension as the objective function is employed to choose the filter length of MOMEDA, and then the feature frequency is extracted by MOMEDA from the reconstructed signal. …”
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Multi-objective optimization of hybrid energy systems using gravitational search algorithm by Sayyed Mostafa Mahmoudi, Akbar Maleki, Dariush Rezaei Ochbelagh

“…Results demonstrate that the GSA outperforms established methods, such as multi-objective particle swarm optimization and non-dominated sorting genetic algorithm II in Pareto front diversity and convergence. …”
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Interconnected Microgrids Load‐Frequency Control Using Stage‐by‐Stage Optimized TIDA+1 Error Signal Regulator by Hossein Shayeghi, Alireza Rahnama, Nicu Bizon, Antoni Szumny

“…The controller parameters are optimized using a modified particle swarm optimization (PSO) algorithm with nonlinear time‐varying acceleration coefficients (NTVAC). …”
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A novel predictive model for the compressive strength development in class G cement slurries at different temperatures, retarder, and salt concentrations by Ali Barati Harooni, Hasan Maroof, Maryam Abdollahi Khoshmardan, Omid Pourali

“…Moreover, a computer-based Radial Basis Function optimized by Particle Swarm Optimization (PSO-RBF) model with three layers was developed for the estimation of experimental compressive strength data. …”
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Research on the Prediction of the Water Demand of Construction Engineering Based on the BP Neural Network by Hao Peng, Han Wu, Junwu Wang

“…To address the large water consumption and high uncertainty of water demand in project construction, a prediction model based on the back propagation (BP) neural network improved by particle swarm optimization (PSO) was proposed in the present work. …”
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Classification of Different Blueberry Cultivars by Analysis of Physical Factors, Chemical and Nutritional Ingredients, and Antioxidant Capacities by Juan Song, Qiong Shi, Si-Min Yan, Hai-Yan Fu, Si-Zhan Wu, Lu Xu

“…A supervised classification method, partial least squares discriminant analysis (PLSDA), was combined with the global particle swarm optimization algorithm (PSO) and two multiclass strategies, one-versus-rest (OVR) and one-versus-one (OVO), to select discriminative quality factors and develop classification models of the 12 cultivars. …”
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Reactive Power Optimization of Distribution Network Considering the Reactive Power Output of Doubly Fed Induction Generator by Cheng ZHANG, Yu CHEN, Can LI, Liangshuai LU, Zhenglong XIA

“…[Result] Taking the improved IEEE33 node distribution network as an example, the improved whale optimization algorithm can improve the global search ability and convergence speed comparing the particle swarm optimization and gray wolf optimizer. [Conclusion] The optimal reactive power output of wind farm optimized by the proposed strategy can reduce more system losses and improve the voltage stability of distribution network.…”
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Comparative Analysis of Shear Strength Prediction Models for Reinforced Concrete Slab–Column Connections by Sarmed Wahab, Nasim Shakouri Mahmoudabadi, Sarmad Waqas, Nouman Herl, Muhammad Iqbal, Khurshid Alam, Afaq Ahmad

“…Compared with the design codes and other machine learning models, the particle swarm optimization-based feedforward neural network (PSOFNN) performed the best predictions. …”
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A Reliable Method for Identification of Antibiotics by Terahertz Spectroscopy and SVM by Jin Guo, Hu Deng, Quancheng Liu, Linyu Chen, Zhonggang Xiong, Liping Shang

“…The model parameters were optimized through grid search (GS), genetic algorithm (GA), and particle swarm optimization (PSO) methods, and the optimal identification results were obtained after comparison across these methods. …”
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A Semi-Analytical Method for the Identification of DC-Decay Parameters at an Arbitrary Rotor Position in Large Synchronous Machines by Zhenming Lai, Haoyu Kang, Demin Liu, Zhichao Wang, Yong Yang, Jin Wang

“…Meanwhile, combining the transient analysis theory and particle swarm optimization algorithm, a semi-analytical parameter identification method is proposed. …”
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Nonpenalty Machine Learning Constraint Handling Using PSO-SVM for Structural Optimization by Marco M. Rosso, Raffaele Cucuzza, Fabio Di Trapani, Giuseppe C. Marano

“…Firstly formulated to solve unconstrained optimization problems, the common way to solve constrained ones with the metaheuristic particle swarm optimization algorithm (PSO) is represented by adopting some penalty functions. …”
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Model Predictive Control for Automatic Carrier Landing with Time Delay by Kaikai Cui, Wei Han, Yujie Liu, Xinwei Wang, Xichao Su, Jie Liu

“…This paper focuses on the problem of automatic carrier landing control with time delay, and an antidelay model predictive control (AD-MPC) scheme for carrier landing based on the symplectic pseudospectral (SP) method and a prediction error method with particle swarm optimization (PE-PSO) is designed. Firstly, the mathematical model for carrier landing control with time delay is given, and based on the Padé approximation (PA) principle, the model with time delay is transformed into an equivalent nondelay one. …”
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Novel Optimized Support Vector Regression Networks for Estimating Fresh and Hardened Characteristics of SCC by Babak Naeim, Ali Javadzade Khiavi, Parisa Dolatimehr, Behnam Sadaghat

“…The goal of this research is to identify the SVR technique's critical parameters utilizing Henry gas solubility optimization ( HGSO) and particle swarm optimization ( PSO). SCC's fresh-phase characteristics include the slump flow, V-funnel test, and L-box test, whereas its hardened-phase features involve the strength of the compressive. …”
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Optimal channel and feature selection for automatic prediction of functional brain age of preterm infant based on EEG by Ling Li, Jiahui Li, Hui Wu, Yanping Zhao, Qinmei Liu, Hairong Zhang, Wei Xu

“…To optimize channel selection, we combine Binary Particle Swarm Optimization (BPSO) with Forward Addition (FA) and Backward Elimination (BE) methods. …”
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A Multiobjective Optimal Operation of a Stand-Alone Microgrid Using SAPSO Algorithm by Guoping Zhang, Weijun Wang, Jie Du, Hua Liu

“…For this purpose, an efficient search algorithm combining the particle swarm optimization (PSO) algorithm and the simulated annealing (SA) algorithm is developed. …”
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Dual-Band Power Divider with Wide Suppression Band by Salman Karimi, Golshan Mohamadpour, Saeed Roshani

“…The weights of each neuron are obtained using particle swarm optimization algorithms. The proposed neural network model has accurate results, and the mean relative error of the train and test data for both outputs is <0.1 , which validates the accurate results of the proposed model. …”
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