Deployment scheme of RSU based on connection time in VANET by Zhengchao DING, Zhenchun WEI, Lin FENG

“…For the roadside unit (RSU) placement problem in vehicular Ad Hoc network (VANET),the deployment scheme of RSU based on connection time was proposed.The scheme find the optimal positions of RSU for maximizing the number of vehicles while ensuring a certain level of connection time under the limited number of RSU.The problem was modeled as a maximum coverage problem,and a binary particle swarm algorithm was designed to solve it.The simulation experiment was carried out with the real Beijing road network map and taxi GPS data.The simulation results show that the algorithm is convergent,stable and feasible.Compared with the greedy algorithm,the proposed scheme can provide continuous network service for more vehicles.…”
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Deployment scheme of RSU based on connection time in VANET by Zhengchao DING, Zhenchun WEI, Lin FENG

“…For the roadside unit (RSU) placement problem in vehicular Ad Hoc network (VANET),the deployment scheme of RSU based on connection time was proposed.The scheme find the optimal positions of RSU for maximizing the number of vehicles while ensuring a certain level of connection time under the limited number of RSU.The problem was modeled as a maximum coverage problem,and a binary particle swarm algorithm was designed to solve it.The simulation experiment was carried out with the real Beijing road network map and taxi GPS data.The simulation results show that the algorithm is convergent,stable and feasible.Compared with the greedy algorithm,the proposed scheme can provide continuous network service for more vehicles.…”
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Forecast-Aided Converter-Based Control for Optimal Microgrid Operation in Industrial Energy Management System (EMS): A Case Study in Vietnam by Yeong-Nam Jeon, Jae-ha Ko

“…The forecasted load data is then used to optimize charge/discharge schedules for energy storage systems (ESS) using a Particle Swarm Optimization (PSO) algorithm. …”
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Performance evaluation of an optimized simplified nonlinear active disturbance rejection controller for rotor current control of DFIG-based wind energy system by Ahmed Sobhy, Medhat Hegazy Elfar, Ahmed Refaat, Mahmoud Fawzi

“…Due to the inherent nonlinear dynamics of DFIG, which increase the system's complexity, conventional proportional-integral (PI) controllers often face limitations in maintaining optimal performance. To address these challenges, an optimized simplified nonlinear active disturbance rejection (SNADR) control strategy, enhanced through the Particle Swarm Optimization (PSO) algorithm for parameter tuning, is proposed. …”
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Research on the Optimal Scheduling of Multi-Microgrid Double-Layer Game Considering Fair Carbon Trading Strategy in the Green Certificate Trading Market by Shuaibo Zhang, Fei He, Baofeng Li

“…The proposed method involves the development of a two-layer optimal scheduling model using the Mixed Integer Chaotic Particle Swarm Optimization algorithm (MICPSO). …”
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A Reliable Approach for Solving Transmission Network Expansion Planning with Objective of Planning Cost Reduction by Yongqiu Liu

“…The particle swarm optimization algorithm searches for optimal planning to reach the fitness requirement. transmission expansion planning problem involves a decision on the location and number of new transmission lines. …”
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Combining miRNA concentrations and optimized machine-learning techniques: An effort for the tomato storage quality assessment in the agriculture 4.0 framework by Seyed Mohammad Samadi, Keyvan Asefpour Vakilian, Seyed Mohamad Javidan

“…The maximum performance of predicting the mechanical loading on the fruits (R2 = 0.91) was obtained by combining the RF with the particle swarm optimization. Also, feature selection results showed that miRNA1917, miRNA172, and miRNA156, as inputs to the optimized RF model could predict the storage temperature, storage period, and mechanical loading on the fruits with R2 values of 0.94, 0.93, and 0.93, respectively. …”
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Multiobjective optimization of CO2 injection under geomechanical risk in high water cut oil reservoirs using artificial intelligence approaches by Fankun Meng, Jia Liu, Gang Tong, Hui Zhao, Chengyue Wen, Yuhui Zhou, Vamegh Rasouli, Minou Rabiei

“…Therefore, a hybrid optimization framework was designed that combines artificial intelligence methods (Support Vector Regression with the Gaussian kernel, Gaussian-SVR or Long Short-Term Memory, LSTM) and multi-objective optimization algorithms (multiple objective particle swarm optimization, MOPSO or Non-dominated Sorting Genetic Algorithm II, NSGA-II) to find the optimal CO2 injection and production strategies under different water cut. …”
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Optimizing intelligent reflecting surface assisted visible light communication networks under blockage and practical constraints using TLBO for IoT applications by Vipul Dixit, Ajit Kumar, Nishant Sharan, Shikha Pandey, Atul Kumar, Ram Sewak Singh

“…Additionally, detailed convergence analysis demonstrates that TLBO performs better than Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) in terms of convergence speed, higher fitness value and lower sensitivity to initial conditions, making it most suitable for real-time IRS-VLC based IoT applications.…”
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CEEMDAN-IHO-SVM: A Machine Learning Research Model for Valve Leak Diagnosis by Ruixue Wang, Ning Zhao

“…Moreover, the performance of IHO was proven to be optimal compared to HO, Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), and Sparrow Search Algorithm (SSA) by calculating twelve test functions. …”
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Design of an intelligent AI-based multi-layer optimization framework for grid-tied solar PV-fuel cell hybrid energy systems by Prashant Nene, Dolly Thankachan

“…The results validate its capability when compared against traditional methods such as Genetic Algorithms and Particle Swarm Optimization. With this, we now have a scalable and real-time energy-efficient solution for future smart grid systems. • Integrated Intelligence Stack: Combines RL-ENN, T-STFREP, FL-DEO, GNNHSCO, and Q-GAN-ESO into a unified architecture for real-time control, forecasting, decentralized optimization, network routing, and synthetic scenario generation. • Real-Time, Scalable, and Privacy-Preserving: Enables adaptive energy dispatch, federated optimization without compromising data privacy, and graph-based power routing, making it suitable for large-scale, smart grid deployments. • Proven Long-Term Performance: Achieved significant improvements over traditional methods (GA, PSO) with 27.5 % lower NPC, 18.2 % reduction in COE, and 30.2 % increase in battery life, validated using 30 years of meteorological data.…”
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Railway Safety Risk Assessment and Control Optimization Method Based on FTA-FPN: A Case Study of Chinese High-Speed Railway Station by Qi Zhang, Yanhui Zhuang, Yuguang Wei, Hao Jiang, Hao Yang

“…Then, it builds up a bi-objective risk control model, making the minimum safety risk level and minimum necessary cost as the objectives, and it designs discrete particle swarm optimization algorithm to solve the risk control model. …”
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A Novel ANN-PSO Method for Optimizing a Small-Signal Equivalent Model of a Dual-Field-Plate GaN HEMT by Haowen Shen, Wenyong Zhou, Jinye Wang, Hangjiang Jin, Yifan Wu, Junchao Wang, Jun Liu

“…This study introduces a novel method that integrates artificial neural networks (ANNs) with the Particle Swarm Optimization (PSO) algorithm to enhance the efficiency and precision of parameter optimization for the small-signal equivalent model of dual-field-plate GaN HEMT devices. …”
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Nonlinear Hysteresis Parameter Identification of Piezoelectric Actuators Using an Improved Gray Wolf Optimizer with Logistic Chaos Initialization and a Levy Flight Variant by Yonggang Yan, Kangqiao Duan, Jianjun Cui, Shiwei Guo, Can Cui, Yongsheng Zhou, Junjie Huang, Geng Wang, Dengpan Zhang, Fumin Zhang

“…Compared to conventional Particle Swarm Optimization (PSO) and standard GWO, the improved algorithm demonstrates faster convergence, higher accuracy, and superior ergodicity, making it a promising tool for solving optimization problems, such as parameter identification in piezoelectric hysteresis systems. …”
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Short‐term electric power and energy balance optimization scheduling based on low‐carbon bilateral demand response mechanism from multiple perspectives by Juan Li, Yonggang Li, Huazhi Liu

“…The enhanced decision tree classifier (EDTC) algorithm is used to predict the electricity consumption behavior of transferable load (TL) users, and an improved particle swarm optimization (PSO) algorithm with “ε‐greedy” strategy is proposed to solve this model. …”
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College psychological stress assessment system based on LabVIEW and WTA integration by Ting Wang

“…The system collects and analyzes electrocardiogram signals of students in different psychological states, and uses Back Propagation neural networks and particle swarm optimization algorithms to evaluate the level of psychological stress. …”
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Global Maximum Power Point Tracking of Photovoltaic Systems Using Artificial Intelligence by Rukhsar, Aidha Muhammad Ajmal, Yongheng Yang

“…According to the benchmarking, a modified particle swarm optimization (PSO) GMPPT algorithm is proposed, and the experimental results validate its ability to achieve GMPPT with faster dynamics and higher efficiency. …”
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Coordinated Control of Relative Orbit of Co-Location Geostationary Satellites Using Game Theory by Fatemeh Amozegay, Amirreza Kosari, Mahdi Fakoor

“…Additionally, a multi-objective particle swarm optimization (MOPSO) algorithm has been used in this article to calculate the optimal initial position of the satellites based on the co-location requirements and the frequency band used in the inter-satellite link. …”
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Research on the Data-Driven Identification of Control Parameters for Voltage Ride-Through in Energy Storage Systems by Liming Bo, Jiangtao Wang, Xu Zhang, Yimeng Su, Xueting Cheng, Zhixuan Zhang, Shenbing Ma, Jiyu Wang, Xiaoyu Fang

“…Focusing on the control characteristics of energy storage converters, a non-intrusive identification method for grid-connected control parameters is proposed based on dynamic trajectory feature extraction and a hybrid optimization algorithm that integrates an improved particle swarm optimization (PSO) algorithm with gradient-based coordination. …”
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PSO-Aided Inverse Design of Silicon Modulator by Zijian Zhu, Yingxuan Zhao, Zhen Sheng, Fuwan Gan

“…As a result, we incorporate the inverse design method with the particle swarm optimization (PSO) algorithm and achieve a G-shaped doping profile for the modulator, exhibiting superior <inline-formula><tex-math notation="LaTeX">$V_{\pi } L$</tex-math></inline-formula> of 0.68 V<inline-formula><tex-math notation="LaTeX">$\cdot$</tex-math></inline-formula>cm and low loss of 9.3 dB/cm. …”
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