Dynamic Stability for Seismic-Excited Earth Retaining Structures Following a Nonlinear Criterion by Jingshu Xu, Jiahui Deng, Zemian Wang, Linghao Qi, Yundi Wang

“…With the application of a genetic algorithm and particle swarm optimization, the optimal upper bound solutions of active earth pressure coefficients were obtained. …”
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The Adaptive-Clustering and Error-Correction Method for Forecasting Cyanobacteria Blooms in Lakes and Reservoirs by Xiao-zhe Bai, Hui-yan Zhang, Xiao-yi Wang, Li Wang, Ji-ping Xu, Jia-bin Yu

“…In addition, the number of nearest neighbors used for modeling was optimized by particle swarm optimization. Finally, a fuzzy linear regression method based on error-correction was used to revise the model dynamically near the operating point. …”
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Voltage Support Capacity Improvement for Wind Farms with Reactive Power Substitution Control by Yuegong Li, Guorong Zhu, Jianghua Lu, Hua Geng

“…Considering differences in terminal voltage characteristics and operating conditions, this RPS control method employs a particle swarm optimization (PSO) algorithm to ensure that wind turbines can provide their optimal reactive power support capacity. …”
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First-Principles and PSO-Driven Exploration of Ca-Pt Intermetallics: Stable Phases and Pressure-Driven Transitions by Yifei Wang, Dengjie Yan

“…In this study, first-principles calculations in conjunction with the particle swarm optimization (PSO) algorithm structure search method were employed to investigate the stable phases of Ca-Pt intermetallic compounds under various pressure conditions. …”
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Using Hybrid Artificial Intelligence Approaches to Predict the Fracture Energy of Concrete Beams by Qinghua Xiao, Congming Li, Shengxiang Lei, Xiangyu Han, Qiaofeng Chen, Zemin Qiu, Biao Sun

“…Then, the hyperparameters were tuned with the particle swarm optimization (PSO) algorithm; the performances of these three optimum models were compared with the test dataset. …”
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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 Power Allocation and Beamforming Design for Active IRS-Aided Secure Directional Modulation Systems by Yifan Zhao, Xiaoyu Wang, Kaibo Zhou, Xuehui Wang, Yan Wang, Wei Gao, Ruiqi Liu, Feng Shu

“…The CF solutions to BS beamforming vectors and IRS reflection coefficient matrix are respectively attained via NSP and MRR algorithms. For the PA factors, we take advantage of exhaustive search (ES) algorithm, particle swarm optimization (PSO) and simulated annealing (SA) algorithm to search for the solutions. …”
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Research on low-energy-consumption deployment of emergency UAV network for integrated communication-navigating-sensing by Li WANG, Qing WEI, Lianming XU, Yuan SHEN, Ping ZHANG, Aiguo FEI

“…In public emergencies such as accident relief, rescue workers are faced with challenges, such as poor communication, unstable navigating, and inaccurate disaster sensing.It is necessary to deploy an emergency unmanned aerial vehicle (UAV) network to guarantee the services of communication-navigating-sensing.Aiming at alleviating the problem of limited energy of UAV, a low-energy-consumption deployment of an emergency UAV network was first proposed for integrated communication-navigating-sensing (ICNS).The proposed scheme was able to realize network topology reconstruction and role cognition on demand.Then, a particle swarm algorithm based hierarchical matching decision-making algorithm was presented to jointly optimize three sub-problems, including the associations between UAVs and users, the resource allocation for multi-role UAV communications, and the UAV position.Simulation results show that the proposed ICNS scheme can achieve flexible adaptation of the multi-objective requirements and limited network resources, and dramatically reduce the demand for the number of UAVs and the deployment energy consumption.…”
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Research on low-energy-consumption deployment of emergency UAV network for integrated communication-navigating-sensing by Li WANG, Qing WEI, Lianming XU, Yuan SHEN, Ping ZHANG, Aiguo FEI

“…In public emergencies such as accident relief, rescue workers are faced with challenges, such as poor communication, unstable navigating, and inaccurate disaster sensing.It is necessary to deploy an emergency unmanned aerial vehicle (UAV) network to guarantee the services of communication-navigating-sensing.Aiming at alleviating the problem of limited energy of UAV, a low-energy-consumption deployment of an emergency UAV network was first proposed for integrated communication-navigating-sensing (ICNS).The proposed scheme was able to realize network topology reconstruction and role cognition on demand.Then, a particle swarm algorithm based hierarchical matching decision-making algorithm was presented to jointly optimize three sub-problems, including the associations between UAVs and users, the resource allocation for multi-role UAV communications, and the UAV position.Simulation results show that the proposed ICNS scheme can achieve flexible adaptation of the multi-objective requirements and limited network resources, and dramatically reduce the demand for the number of UAVs and the deployment energy consumption.…”
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Integration of Multi-Source Landslide Disaster Data Based on Flink Framework and APSO Load Balancing Task Scheduling by Zongmin Wang, Huangtaojun Liang, Haibo Yang, Mengyu Li, Yingchun Cai

“…The present study proposes an innovative approach to integrate multi-source landslide disaster data by combining the Flink-oriented framework with load balancing task scheduling based on an improved particle swarm optimization (APSO) algorithm. It utilizes Flink’s streaming processing capabilities to efficiently process and store multi-source landslide data. …”
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A deep decentralized privacy-preservation framework for online social networks by Samuel Akwasi Frimpong, Mu Han, Emmanuel Kwame Effah, Joseph Kwame Adjei, Isaac Hanson, Percy Brown

“…Our methodology employs a two-tier architecture: the first tier uses an elitism-enhanced Particle Swarm Optimization and Gravitational Search Algorithm (ePSOGSA) for optimizing feature selection, while the second tier employs an enhanced Non-symmetric Deep Autoencoder (e-NDAE) for anomaly detection. …”
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An accurate model to predict drilling fluid density at wellbore conditions by Mohammad Ali Ahmadi, Seyed Reza Shadizadeh, Kalpit Shah, Alireza Bahadori

“…In this regard, a couple of particle swarm optimization (PSO) and artificial neural network (ANN) was utilized to suggest a high-performance model for predicting the drilling fluid density. …”
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Investigation on the Role of Artificial Intelligence in Measurement System by P. A. Rezvy, Venkata Lakshmi Narayana Komanapalli

“…Hardware approach with soft computation has reduced non linearity error by 84.63% for thermocouple linearization, meanwhile novel hybrid approach using genetic algorithm (GA) and particle swarm optimization (PSO) combined with back propagation neural network (BPNN) have reduced mean absolute percentage error to 1.2 % for industrial weir than conventional hardware approaches using sensors and signal conditioning circuits but at higher computational cost. …”
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Wavenumber-Domain Joint Estimation of Rotation Parameters and Scene Center Offset for Large-Angle ISAR Cross-Range Scaling by Bakun Zhu, Weigang Zhu, Hongfeng Pang, Chenxuan Li, Lei Qui, Jinhai Yan, Fanyin Ma, Yijia Liu

“…Utilizing this model and the sensitivity of wavenumber-domain imaging to SCO, a joint estimation algorithm that combines particle swarm optimization (PSO) and image entropy evaluation is proposed, achieving accurate parameter estimation. …”
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A Distribution Model for Shared Parking in Residential Zones that Considers the Utilization Rate and the Walking Distance by Wenhui Zhang, Fan Gao, Shurui Sun, Qiuying Yu, Jinjun Tang, Bohang Liu

“…The second objective is the acceptable walking distance from the parking space to the destination. The particle swarm optimization (PSO) algorithm is used to solve this model. …”
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HGAPSO-Based Third Order-SMC, ST-SMC, and SMC Strategy for AAV Control: A Comparative Analysis by Dawit Kefale Wassie, Lebsework Negash Lemma, Abrham Tadesse Kassie

“…Therefore, a hybrid type of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), HGAPSO, has been formulated to find the best controllers’ parameters. …”
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A PSO-XGBoost Model for Predicting the Compressive Strength of Cement–Soil Mixing Pile Considering Field Environment Simulation by Jiagui Xiong, Yangqing Gong, Xianghua Liu, Yan Li, Liangjie Chen, Cheng Liao, Chaochao Zhang

“…Utilizing data mining on 84 sets of experimental data with various preparation parameter combinations, a prediction model for the as-formed strength of CSM Pile was developed based on the Particle Swarm Optimization-Extreme Gradient Boosting (PSO-XGBoost) algorithm. …”
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Voltage and Current Balancing of a Faulty Photovoltaic System Connected to Cascaded H-Bridge Multilevel Inverter by Kamel Djermouni, Ali Berboucha, Salah Tamalouzt, Djamel Ziane

“…For such a system, the particle swarm optimization (PSO) algorithm remains highly effective because it can easily handle the existence of multiple maxima simultaneously to provide the best possible solution. …”
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Trajectory Planning Method for Formation Rendezvous of Underactuated Multi-UUV Under Multiple Constraints by Qingzhe Wang, Da Xu, Xiaoran Liu, Gengshi Zhang, Zhao Han

“…In response to these issues, this paper presents a rendezvous points allocation method and a trajectory planning method for formation rendezvous based on dynamic parameter particle swarm optimization (DPPSO) optimizing polynomial trajectories. …”
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Research on Sensitivity Improvement Methods for RTD Fluxgates Based on Feedback-Driven Stochastic Resonance with PSO by Rui Wang, Na Pang, Haibo Guo, Xu Hu, Guo Li, Fei Li

“…Simulink is used to construct the sensor model of odd polynomial feedback control, and the Particle Swarm Optimization (PSO) algorithm is used to optimize the coefficients of the feedback function so that the sensor reaches a resonance state, thus reducing the noise interference and improving the sensitivity of the sensor. …”
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