Electric Vehicle Cluster and Scheduling Strategy Based on Dynamic Game by LI Shuai, DING Xiying, JIANG Hongfa

“…The upper layer takes the peak shaving demand and peak shaving cost of distribution system operator (DSO) as the optimization objectives, and uses an improved multi-objective particle swarm optimization algorithm to obtain the game strategy set of DSO. …”
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Analysis of Sub-Synchronous Oscillation in Grid-Connected Wind Farm and Proposed Improved Solution by Trong Nghia Le, Chau Le Thi Minh, Phuong Nam Nguyen, Vu Nguyen Hoang Minh

“…Therefore, this paper proposes optimizing the internal control parameters of the RSC using meta-heuristic algorithms, including Particle Swarm Optimization (PSO), Cuckoo Search Algorithm (CSA), and Ant Colony Optimization (ACO). …”
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Advanced Machine Learning Methodology for Earthquake Magnitude Forecasting Using Comprehensive Seismic Data by Subhieh El-Salhi, Bashar Igried, Sari Awwad

“…Feature selection was performed using Genetic Algorithm, Particle Swarm Optimization, and Simulated Annealing, while ten machine learning models were implemented — ranging from Linear Regression and Decision Trees to Gradient Boosting, XGBoost, LightGBM, and Long Short-Term Memory (LSTM) networks. …”
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A Method for Service Function Chain Migration Based on Server Failure Prediction in Mobile Edge Computing Environment by Joelle Kabdjou, Norihiko Shinomiya

“…Using a Long Short-Term Memory (LSTM) algorithm optimized by Super SAPSO (Simulated Annealing Particle Swarm Optimization), the model forecasts server failures with improved accuracy, reducing False Alarm Rates and improving Failure Detection Rates. …”
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Blasting vibration velocity prediction of open pit mines based on GRA-EPSO-SVM model by Pengfei ZHANG, Yong YUAN, Yunhua HE, Shaojun DAI, Jiazhen LI, Xuehai CHI, Wei LI, Xue SUN, Jiao ZHANG, Runcai BAI, Honglu FEI

“…Based on the coal and rock blasting in Yuanbaoshan open-pit coal mine under different occurrence conditions, hole spacing, row spacing, hole depth, maximum charge in single section, minimum resistance line, blast center spacing, elevation difference and peak particle vibration velocity were selected as input parameters, and grey correlation analysis (GRA) was used to filter redundant factors affecting peak blasting vibration velocity (hole depth, maximum charge of single section, minimum resistance line, peak particle velocity); using integrated particle swarm optimization algorithm (EPSO) to optimize the key parameters C and g of SVM algorithm, and inputting the parameters into GRA-EPSO-SVM model for evaluation. …”
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A novel multi-task learning model based on Transformer-LSTM for wind power forecasting by Rongquan Zhang, Siqi Bu, Yuxia Zheng, Gangqiang Li, Xiupeng Wan, Qiangqiang Zeng, Min Zhou

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Frequency stabilization of a sophisticated multi-area interconnected hybrid power system considering non-inertia sources by Diaa M. Gawad, Gaber Magdy, M.A. Ebrahim, Eduard Petlenkov

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Synthesis of the Spatial Arrangement of Magnetic Field Sensors for Active Magnetic Field Shielding Systems of Overhead Power Lines by Kuznetsov B.I., Nikitina T.B., Bovdui I.V., Voloshko O.V., Kolomiets V.V., Kobylianskyi B.B.

“…Solution of the minimax vector optimization problem, calculated on the basis of optimization algorithms for a multi-swarm of particles from Pareto-optimal solutions, taking into account the parameters of binary relations. …”
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Experimental realization of PSO-based hybrid adaptive sliding mode control for force impedance control systems by Sarucha Yanyong, Somyot Kaitwanidvilai

“…A sliding surface guarantees system stability, while Particle Swarm Optimization (PSO) optimizes impedance parameters, reducing the risk of local minima. …”
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Multi-head surface mounting placement optimisation based on adaptive multi-point crossover operator by Song Zhiqiang, Zhang Chunfu

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Evaluation Model of Low-Carbon Circular Economy Coupling Development in Forest Area Based on Radial Basis Neural Network by Chang Liu

“…In this paper, we study the radial neural network algorithm for low-carbon circular economy in forest area, design a coupled development evaluation model, study its algorithmic ideas operation mode and the update formula obtained by standard algorithm, and finally optimize the RBF neural network by particle swarm algorithm. …”
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Research on over-the-horizon air combat guidance method based on dynamic RCS by SONG Shaomei, YU Yifei, LI Gai, YANG Qiming, ZHANG Jiandong

“…Then, to maximize the situation assessment value as the goal, particle swarm optimization algorithm is used to find the most appropriate overload at every moment, so as to guide our aircraft into the attack zone to constitute the launch conditions. …”
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A non-line-of-sight error mitigation method for location estimation by Chien-Sheng Chen

“…The proposed algorithm uses the intersections of three time-of-arrival circles based on the particle swarm optimization technique to give a location estimation of the mobile station in non-line-of-sight environments. …”
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A High-Precision Real-Time Temperature Acquisition Method Based on Magnetic Nanoparticles by Yuchang Zhu, Li Ke, Yijing Wei, Xiao Zheng

“…Additionally, under dual-frequency superimposed magnetic field excitation, a higher temperature inversion accuracy is achieved compared with that of the particle swarm optimization–gray wolf optimization algorithm, reducing the error from 0.237 K to 0.094 K.…”
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Challenges of International Trade and Government Governance from the Perspective of Economic Globalization by Dan Ge

“…On the basis of expounding the particle swarm optimization algorithm and GMDH algorithm, the optimization mode, method, and process of GMDH network based on particle swarm optimization are also expounded. …”
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Environmental and Economic Dispatching of Fire-Wind Combined System Based on Improved MOPSO by Li Rongshuai, Du Ruiqi, Lu Yi, Jiang Shan

“…Then, an improved multi-objective particle swarm optimization algorithm, LRMOPSO, is proposed by combining multi-objective particle swarm optimization algorithm, Levy flight jamming strategy and reverse learning strategy. …”
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基于粒子群优化的形态学滤波器消噪方法 by 吴国洋

“…Aiming at removing the noise of the vibration signal,a new mathematical morphological filter method is proposed,the proposed filter is optimized by particle swarm optimization algorithm.Firstly,the morphological filter is constructed according to the characteristic of morphological algorithm.Then,the particle swarm optimization algorithm is used to select the adaptive structural element,which has an important role in morphological filter,and the maximum signal-noise-ratio is used as the criteria of the optimization process,achieving to get the optimal structural element.Finally,the simulation experiment and the bearing fault signal are analyzed,the results show that the optimal morphological filter is better in removing the noise than the wavelet method and the traditional morphological filter methods,the proposed method can effectively reduce the noise of the mechanical equipment.…”
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Multimode Resource-Constrained Multiple Project Scheduling Problem under Fuzzy Random Environment and Its Application to a Large Scale Hydropower Construction Project by Jiuping Xu, Cuiying Feng

“…Then a combinatorial-priority-based hybrid particle swarm optimization algorithm is developed to solve the proposed model, where the combinatorial particle swarm optimization and priority-based particle swarm optimization are designed to assign modes to activities and to schedule activities, respectively. …”
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Fault Diagnosis of Gearbox based on Multi-fractal and PSO-SVM by Li Sha, Pan Hongxia, Zhang Jundong, Zhao Weiwei

“…Aiming at the non-stationary and nonlinear of gearbox vibration signals,a fault diagnosis method based on the multi-fractal and particle swarm optimization support vector machine（PSO-SVM）is put forward.Firstly,the fractal filter with short-time fractal dimension as fuzzy control parameters is used to filtering noise reduction the gearbox vibration signals with bigger background noises.Secondly,the multi-fractal spectrum algorithm is applied to analyze the signal after filtering,the results show that the characteristic parameters:Δa（q）、f（a（q））maxand box dimensions Dbcan give a good presentation for gearbox working condition.Finally,the particle swarm optimization（PSO）is applied to optimize the parameters of support vector machine（SVM）.Taking the multi-fractal characteristic vectors as input parameters of PSO-SVM and SVM to recognize the fault types of the gearbox.The results show that SVM based on particle swarm optimization can improve the classification accuracy.Meanwhile,the validity of gearbox fault diagnosis based on muti-fractal and PSO-SVM is proved.…”
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Classification Prediction of Rockburst in Railway Tunnel Based on Hybrid PSO-BP Neural Network by Min Zhang

“…Then, the BP neural network is improved by using particle swarm optimization (PSO) combined with the simulated annealing algorithm. …”
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