Novel intelligent adaptive sliding mode control for marine fuel cell system via hybrid algorithm by Shiyi Fang, Daifen Chen, Xinyu Fan

“…The control strategy employs a non-singular fast terminal sliding surface for the controller, integrating a fuzzy logic and particle swarm optimization to tune the sliding mode gain and dynamically regulate output, thereby enhancing system efficiency and minimizing energy consumption. …”
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A Hybrid Chatter Detection Method Based on WPD, SSA, and SVM-PSO by Erhua Wang, Peng Yan, Jie Liu

“…Furthermore, the support vector machine model is optimized by particle swarm optimization to recognize the cutting states in end milling. …”
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Object Boundary Detection Using Active Contour Model via Multiswarm PSO with Fuzzy-Rule Based Adaptation of Inertia Factor by Ajay Khunteta, D. Ghosh

“…One such evolutionary algorithm that has been used extensively in active contours is the particle swarm optimization (PSO). However, conventional PSO converges slowly and gets trapped in local minimum easily which results in inaccurate detection of concavities in the object boundary. …”
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Research and Model Prediction on the Performance of Recycled Brick Powder Foam Concrete by Hongyang Xie, Jianjun Dong, Yong Deng, Yiwen Dai

“…Five machine learning models, namely, back propagation neural network improved by particle swarm optimization (PSO-BP), support vector machine (SVM), multiple linear regression (MLR), random forest (RF), and back propagation neural network (BP), were used to predict the compressive strength of recycled brick powder foam concrete, and the PSO-BP model was found to have obvious advantages in terms of prediction accuracy and model stability. …”
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Algorithm PSO for maximum coverage of users using drones to find optimal points for deployment by Fariborz Rasouli

“…For this reason, using the Particle Swarm Optimization (PSO) algorithm, we try to introduce drones as ABS to find the maximum coverage of users in the shortest possible time. …”
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Application of Harris Hawks Optimization Algorithm in Optimization of Generalized Nonlinear Muskingum Parameters ——A Case Study of the Luohe River by CHEN Haitao, ZHAO Zhijie

“…The Muskingum model plays an important role in river flood simulation,and its simulation accuracy relies on the optimal selection of parameters.To address the current challenges in parameter calibration for the Muskingum model,such as complex solution processes and low accuracy,the use of the Harris Hawks optimization (HHO) algorithm was proposed to optimize its parameters.HHO algorithm has a wide range of global search capabilities,with fewer parameters to be adjusted.Taking Luohe River,a tributary of the Yellow River,as the research object,the generalized nonlinear Muskingum model was used to simulate the flood in the Yiyang-Baimasi section of the river.The parameters were optimized by employing the HHO algorithm,particle swarm optimization (PSO) algorithm,and ant colony optimization (ACO) algorithm,respectively.The results show that the generalized nonlinear Muskingum model based on the HHO algorithm achieved high simulation accuracy in the Yiyang-Baimasi section of the Luohe River,with a Min.SSD of 1 237 and the flood peak error (DPO) of only 5,outperforming those obtained through optimization using PSO algorithm and ACO algorithm.The results are suitable for application in flood forecasting in the Yiyang-Baimasi section of the Luohe River.…”
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A Quality Control Method Based on an Improved Kernel Regression Algorithm for Surface Air Temperature Observations by Xiaoling Ye, Yajin Kan, Xiong Xiong, Yingchao Zhang, Xin Chen

“…An improved kernel regression (IKR) method based on an adaptive algorithm and particle swarm optimization is proposed. Considering the limitations of current quality control methods in different regions and on multiple time scales, the kernel regression algorithm is applied to the quality control of surface air temperature observations. …”
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Improved dynamic programming method for solving multi-objective and multi-stage decision-making problems by Zhihao Liang, Kegang Zhao, Kunyang He, Yanwei Liu

“…The results demonstrate that the NSDP algorithm achieves better outcome in multiple performance metrics and higher solving efficiency, compared with non-dominated sorting genetic algorithm II and multi-objective particle swarm optimization.…”
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Conformal Array Pattern Synthesis and Activated Elements Selection Strategy Based on PSOGSA Algorithm by Bin Sun, Chunheng Liu, Yang Liu, Xiaofang Wu, Yongzhen Li, Xuesong Wang

“…The pattern synthesis and activated element selection for conformal array is investigated based on hybrid particle swarm optimization-gravitational search algorithm (PSOGSA) in this paper. …”
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Project Scheduling Heuristics-Based Standard PSO for Task-Resource Assignment in Heterogeneous Grid by Ruey-Maw Chen, Chuin-Mu Wang

“…Hence, this investigation introduces a named “standard“ particle swarm optimization (PSO) metaheuristic approach to efficiently solve the task scheduling problems in grid. …”
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Multi-UUV Cooperative Dynamic Maneuver Decision-Making Algorithm Using Intuitionistic Fuzzy Game Theory by Lu Liu, Lichuan Zhang, Shuo Zhang, Sheng Cao

“…Meanwhile, the modified particle swarm optimization method is presented to solve the established problem and find the optimal strategy. …”
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Analysis and Implementation of Optimization Techniques for Facial Recognition by Justice Kwame Appati, Huzaifa Abu, Ebenezer Owusu, Kwaku Darkwah

“…The resultant features were optimized using the particle swarm optimization (PSO) algorithm. For the purpose of performance comparison, the resultant features were also optimized with the genetic algorithm (GA) and the artificial bee colony (ABC). …”
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Study on the Optimization of Hub-and-Spoke Logistics Network regarding Traffic Congestion by Wei Xu, JinCan Huang, YanZhao Qiu

“…Given the complexity of the problem, the congestion cost function is linearized, and the mutational particle swarm optimization (MPSO) is employed for the solution. …”
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Optimization and Modeling of Optical Emission Spatial Coverage from Underwater Multi-Faceted Optical Base Stations by Junjie Shi, Chunbo Ma, Xu Tian, Hanjun Guo, Jun Ao

“…Additionally, a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is used to optimize the configuration of the multi-faceted LED array by adjusting the deflection angles of the LED arrays and the emission half-angle of the LEDs at the OBS. …”
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Multicompare Tests of the Performance of Different Metaheuristics in EEG Dipole Source Localization by Diana Irazú Escalona-Vargas, Ivan Lopez-Arevalo, David Gutiérrez

“…We study the use of nonparametric multicompare statistical tests on the performance of simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE), when used for electroencephalographic (EEG) source localization. …”
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Medium- and Long-term Runoff Prediction Based on SMA-LSSVM by TIAN Jinghuan, LI Congxin, LI Ang

“…Medium-and long-term runoff prediction is extremely important for flood control,disaster reduction and the utilization efficiency improvement of water resources.To avoid the influence of prediction model parameters on prediction accuracy,this paper proposes a medium-and long-term runoff prediction model based on least squares support vector machine (LSSVM) optimized by the slime mold algorithm (SMA).Firstly,five standard test functions are selected to compare the simulation results of SMA and particle swarm optimization (PSO) algorithms in different dimensions.Secondly,SMA is used to optimize the penalty parameters and kernel parameters of LSSVM,and the comparison models of LSSVM and PSO-LSSVM are constructed.Finally,the models are verified with the monthly runoff of Manwan Hydropower Station Reservoir and Yingluoxia Hydrological Station as prediction examples.The results show that the mean square error of the SMA-LSSVM model is 29.26% and 7.42% lower than those of the LSSVM and PSO-LSSVM models,respectively,in the monthly runoff prediction of the Manwan station,and 32.61% and 6.61% lower,respectively,in the monthly runoff prediction of the Yingluoxia station.The proposed SMA-LSSVM model has better comprehensive prediction performance and also provides a new method for medium- and long-term runoff prediction.…”
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An Improved Genetic Algorithm Based Robust Approach for Stochastic Dynamic Facility Layout Problem by Yunfang Peng, Tian Zeng, Lingzhi Fan, Yajuan Han, Beixin Xia

“…Different sized instances are compared with Particle Swarm Optimization (PSO) algorithm to verify the effectiveness of the proposed genetic algorithm. …”
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Enhancing AI-Inspired Analog Circuit Design: Optimizing Component Sizes with the Firefly Algorithm and Binary Firefly Algorithm by Trang Hoang

“…Comparative analysis with existing optimization methods, including Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), underscores the efficiency and accuracy of BFA in optimizing circuit metrics, particularly in power-constrained environments. …”
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Research on the Fault Diagnosis Method for Rolling Bearings Based on Improved VMD and Automatic IMF Acquisition by Ying Zhang, Anchen Wang

“…The mode number K and the penalty parameter α of VMD are automatically obtained through an optimal parameter searching process underpinned by the improved particle swarm optimization (PSO) algorithm with a variety of inertia weights. …”
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An Ensemble Feature Selection Approach-Based Machine Learning Classifiers for Prediction of COVID-19 Disease by Md. Jakir Hossen, Thirumalaimuthu Thirumalaiappan Ramanathan, Abdullah Al Mamun

“…Different feature selection approaches including chi-square test, recursive feature elimination (RFE), genetic algorithm (GA), particle swarm optimization (PSO), and random forest are evaluated for their effectiveness in enhancing the classification accuracy of the machine learning classifiers. …”
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