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MPSO: An optimization algorithm for Task Offloading in Cloud-Edge Aggregated Computing Scenarios for Autonomous Driving

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MOBILE NETWORKS & APPLICATIONS 2024年 1-15页

作者： Liu, Xuanyan Yan, Rui Kim, Jung Yoon Xu, Xiaolong Nanjing Univ Posts & Telecommun Jiangsu Key Lab Big Data Secur & Intelligent Proc Nanjing Peoples R China Gachon Univ Coll Future Ind Seongnam Si South Korea

With the development of cloud computing and edge computing technologies, these technologies have come to play a crucial role in the field of autonomous driving. The autonomous driving sector faces unresolved issues, with one key problem being the handling of latency-sensitive applications within vehicles. Cloud computing and edge computing provide a solution by segmenting unresolved computing tasks and offloading them to different computing nodes, effectively addressing the challenges of high concurrency through distributed computing. While the academic literature addresses computation offloading issues, it often focuses on static scenarios and does not fully leverage the advantages of cloud computing and edge computing. To address these challenges, a multivariate particle swarm optimization (MPSO) algorithm tailored for the cloud-edge aggregated computing environment in the autonomous driving domain is proposed. The algorithm, grounded in real-world scenarios, considers factors that may impact computation latency, abstracts them into quantifiable attributes, and determines the priority of each task. Tasks are then assigned to optimal computing nodes to achieve a balance between computation time and waiting time, resulting in the shortest total average weighted computation latency time for all tasks. To validate the effectiveness of the algorithm, experiments were conducted using the self-designed CETO-Sim simulation platform. The algorithm's results were compared with those of simulated annealing, traditional particle swarm optimization, purely local computation, and purely cloud-based computation. Additionally, comparisons with traditional algorithms were considered in terms of iteration count and result stability. The results indicate that the MPSO algorithm not only achieves optimal computation offloading strategies within specified time constraints when addressing computation offloading issues in the autonomous driving domain but also exhibits high stability. F

关键词： Autonomous driving Cloud-edge aggregated computing Computation offloading particle swarm optimization algorithm Latency optimization

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COVID-19 Vaccines distribution routes with bioinspired metaheuristic algorithms: Resoluteness

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IEEE LATIN AMERICA TRANSACTIONS 2024年第6期22卷 484-493页

作者： Estrada Moreno, Leonardo Daniel Valdovinos Rosas, Rosa Maria Hernandez, Lourdes Loza Alejo, Roberto Univ Autonoma Estado Mexico Toluca Mexico Inst Tecnol Toluca Metepec Mexico

The global emergency of COVID-19 caused by the SARS-CoV-2 virus at the end of 2019, was without a doubt a critical and historical point for society in general;for instance, the effective development of vaccines, as well as the efficient distribution of them;They were an unprecedented challenge to slow down the spread or mitigate its impact on societies around the world. This article specifies three bio-inspired metaheuristic algorithms (genetic algorithm, particle swarm optimization algorithm, and artificial bee colony algorithm) that were used in the context of the capacitated vehicle routing problem to generate vaccine distribution routes, specifically, COVID-19 vaccine for over 18 years old the first and the second doses applications in Mexico, particularly in the State of Mexico. The quality of the solutions obtained by these algorithms is compared, as a result of the performance of the particle swarm optimization (PSO) algorithm being superior in solution quality.

关键词： Vaccines Costs Metaheuristics COVID-19 Vehicle routing Transportation Supply chains Artificial Bee Colony algorithm Bio-inspired Capacitated Vehicle Routing Problem Genetic algorithm particle swarm optimization algorithm

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An Optimal Rotating Machinery Fault Feature Extraction Method Based on the Variational Mode Extraction and Synthetic Detection Index

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JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES 2025年第1期13卷 1-18页

作者： Lu, Na Jiang, Shuangyun Li, Zhongliang Cao, Chaofan Zhang, Guangtao Chen, Xudong Zhengzhou Univ Zhengzhou Peoples R China

PurposeSetting parameters for variational mode extraction (VME) has been a challenge in fault feature extraction. Previous studies have often optimized these parameters using fitness functions like entropy value or kurtosis to evaluate the VME decomposition results. However, these methods overlook the impact on subsequent fault classification. This paper aims to propose a more effective method for fault feature extraction in rotating *** fault feature extraction method based on VME and the synthetic detection index (SDI) is introduced. The VME parameters are optimized using the particle swarm optimization (PSO) algorithm, with the maximum value of SDI as the objective function. The optimized VME is then used to extract features from the original signals and build a fault feature set for fault *** proposed method was tested on two datasets, and the numerical results demonstrate its superior performance in fault classification compared to other *** results show that the proposed method, which optimizes VME parameters using PSO, enhances fault feature extraction and classification accuracy in rotating machinery, outperforming other comparative techniques.

关键词： Rotating machinery Feature extraction Fault diagnosis Variational mode extraction particle swarm optimization algorithm Synthetic detection index

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Construction of Agricultural Economic-Level Prediction Model Based on GWO-SVM algorithm

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INTERNATIONAL JOURNAL OF AGRICULTURAL AND ENVIRONMENTAL INFORMATION SYSTEMS 2025年第1期16卷

作者： Li, Menghua Hebi Automot Engn Profess Coll Hebi Peoples R China

In response to the inability of traditional economic predication models to accurately predict agricultural economy, a method based on improved grey wolf optimization algorithm combined with support vector machine is proposed to establish an agricultural economy predication model. By analyzing the advantages and disadvantages of the grey wolf optimization algorithm, particle swarm optimization algorithm is used to improve the grey wolf optimization algorithm and enhance its search ability. Combined with support vector mechanism, a grey wolf optimization algorithm support vector machine prediction model is established. The results indicate that the proposed model can effectively perform economic forecasting, with a fast convergence speed and the ability to find the global optimal solution after 150 iterations. Overall, the research proposed model can raise the accuracy and efficiency of economic predication.

关键词： GWO SVM Agricultural Economic Level Predictive Model particle swarm optimization algorithm

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optimization of Control Strategy for Fuel Cell Vehicles by Integrating Fuzzy algorithm

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IEEE ACCESS 2024年 12卷 141952-141965页

作者： Wu, Qiong Chen, Hua Liu, Baolong Xian Technol Univ Sch Mechatron Engn Xian 710021 Peoples R China

Fuel cell vehicles have rapidly occupied the market with advantages such as environmental protection and energy conservation. However, their battery technology is insufficient and their endurance is poor, making them unsuitable for use over long distances. To address the aforementioned issues, a fuel cell vehicle energy storage system based on super-capacitors was constructed. Meanwhile, a proportional integral derivative controller based on fuzzy algorithms was established. Finally, the particle swarm optimization algorithm was used to optimize the fuzzy control strategy that integrated the fuzzy algorithm. When using the optimized fuzzy control strategy for simulation, the peak power of the fuel cell output power was reduced from 3.8kW to 2.0kW. The remaining power of the super-capacitor remained stable within a reasonable range throughout the entire operating condition. Under the new European urban road cycle, the optimized control strategy improved energy recovery performance by 4.3% and reduced hydrogen consumption by 0.9964%. Under the United States federal environmental protection agency standardized urban cycle conditions, the optimized control strategy improved the braking energy recovery efficiency index and effective braking energy recovery efficiency by 8.9% and 6.3%, respectively. The percentage reduction in hydrogen consumption was 0.9433%. Therefore, this research method can effectively reduce hydrogen consumption and improve the product economy and market competitiveness of enterprises.

关键词： Batteries Fuel cells Energy storage Mathematical models Supercapacitors Fuel cell vehicles Hydrogen Voltage Periodic structures optimization Environmental monitoring Fuzzy algorithm fuel cell vehicles optimization of control strategy particle swarm optimization algorithm super-capacitor

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Application of Fuzzy Predictive Control for Smoke Sensing Alarm in Automatic Train Driving Control

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IEEE ACCESS 2024年 12卷 150719-150738页

作者： Huang, Peili Zhengzhou Police Univ Sci Publ Order Dept Zhengzhou 450000 Peoples R China

The efficacy of traditional smoke detection methods is often constrained by the use of fixed threshold judgments, which impedes the ability of these methods to accurately and timely detect smoke, thereby increasing the risk of train operation. The objective of this study is to enhance the precision and reliability of automated driving control, thereby enabling vehicles to adapt to diverse and challenging environments more effectively. Based on fuzzy predictive control and granular computing, the algorithm is improved to solve the problem of rule explosion. Combining the annealing algorithm and particle swarm optimization algorithm to optimize the system parameters, a new fuzzy predictive control system for smoke sensing alarms is designed. The experimental results showed that the proposed algorithm on the Rosenbrook algorithm was concentrated between 45 and 51, demonstrating good stability and GSC, and having an excellent ability to jump out of local optima. Furthermore, the model demonstrated a rapid and stable convergence of the prediction accuracy curve, with an average prediction accuracy of 95.1% and a final value of 97.8%. The proposed smoke sensing alarm fuzzy predictive control has the advantages of being less prone to local optima, high accuracy, short time consumption, and fast response in automatic driving control. It provides new solutions and methods for other similar problems in the field of intelligent transportation.

关键词： Prediction algorithms Accuracy Fuzzy logic Safety optimization Predictive control Temperature sensors Reliability Predictive models Sensors particle swarm optimization Smoke detectors Fuzzy predictive control annealing algorithm particle swarm optimization algorithm smoke alarm metropolis guidelines

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Torque ripple reduction strategy for switched reluctance motor based on segmented non-linear correction torque sharing function

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JOURNAL OF POWER ELECTRONICS 2024年第12期24卷 1933-1943页

作者： Ben, Tong Wang, Hongliang Chen, Long Zhang, Yongchao Jing, Libing China Three Gorges Univ Coll Elect Engn & New Energy Yichang Peoples R China China Three Gorges Univ Hubei Prov Engn Technol Res Ctr Power Transmiss Li Yichang Peoples R China

The traditional torque sharing function (TSF) strategy can lead to large torque ripple and copper consumption in switched reluctance motors (SRMs) due to the limitation of the voltage in the phase change interval. In this paper, an improvement scheme is proposed to address this problem. First, a segmented non-linear correction TSF (SN-TSF) is proposed. Then with the torque ripple and copper consumption of the SRM as optimization objectives, the proposed SN-TSF values are optimized using the velocity control particle swarm optimization (VCPSO) algorithm based on the particle velocity-boundary relationship. The optimal parameters are used to obtain the optimal SN-TSF curves with different loading torques and speeds. Experimental results show that the proposed SN-TSF strategy reduces the torque ripple by 40.17% and 66.5%, and the peak phase currents by 5.058 A and 5.371 A under two different operating conditions, which demonstrates the effectiveness of this strategy.

关键词： SRM Torque sharing function particle swarm optimization algorithm Copper consumption

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UAV based device to device communication for 5G/6G networks using optimized deep learning models

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WIRELESS NETWORKS 2024年第8期30卷 7137-7151页

作者： Luo, Yixin Fu, Gui Civil Aviat Flight Univ China Inst Elect & Elect Engn Guanghan 618300 Peoples R China

The success of future 5G/6G communication relies heavily on efficient and low-latency wireless cellular device-to-device communication, which enhances network spectrum, energy efficiency, and data transfer rate. To achieve this, advanced technologies such as deep learning and swarm optimization are utilized to improve energy-efficient communication while considering latency. The fifth and beyond-generation networks require intelligent and efficient technologies to control and transfer data. Unmanned Aerial Vehicles (UAVs) are employed to establish high-transfer-rate data communication between devices using minimal energy consumption. This study also focuses on integrating UAV-based D2D communication with other advanced technologies, highlighting the significance of integration with possible solutions to enhance network performance. The development of 5G/6G communication technologies aims to meet the increasing demands of wireless networks, such as faster data rates, lower latency, improved reliability, and more excellent device and application support. However, the current network advantages need to be enhanced to meet the needs of the upcoming digital environment. Thus, to address these challenges, we propose a novel approach that utilizes optimized deep learning models in three ways for UAV-based device-to-device communication: 1. Improved Hybrid particle swarm optimization and Effective K-means clustering approach (IHPSO-K) 2. Hybrid Fuzzy C means (HFCM) approach, and 3-greedy algorithm to overcome the restrictions faced by UAVs in meeting the latest technological requirements. With the above considering features, we use two methods to execute the efficient performance of the proposed algorithm, namely the device-centric approach and the network-centric approach. Combining these techniques can help achieve a more accurate and efficient clustering of data points in UAV-based D2D communication. This can lead to improved performance in terms of throughput, latency,

关键词： UAV 6G communication Device-to-device communication Energy efficiency Beyond 5G networks particle swarm optimization algorithm K means clustering Fuzzy C means

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Structural optimization Design of MEMS Piezoelectric Hydrophones Based on Intelligent Learning algorithm

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IEEE SENSORS JOURNAL 2024年第13期24卷 20769-20776页

作者： Zhang, Ying Yang, Hua Mao, Beibei Sun, Fei Chen, Zhe Ocean Univ China Dept Elect Engn Qingdao 266100 Peoples R China

Hydrophones rely on high sensitivity to detect weak hydroacoustic signals, with the diaphragm structural design significantly impacting the sensitivity of MEMS piezoelectric hydrophones. Traditional optimization methods for hydrophone structure involve creating an equivalent circuit mathematical model or a finite-element analysis (FEA) model through simulation, starting with a structural model based on equivalent analogy or empirical knowledge, and proceeding with a parametric analysis of geometric structural parameters. However, these methods are computationally intensive and time-consuming, impeding structural design optimization. This article presents an intelligent learning (IL) algorithm-based method for optimizing MEMS hydrophone structures, which aims to simplify the design process and enhance hydroacoustic reception. Orthogonal tests and FEA models are utilized to collect the simulation data, and neural network models are developed to illustrate the relationship between the structural parameters and hydrophone performance. The diaphragm electrode thickness, piezoelectric layer thickness, diaphragm radius, and top electrode radius are optimized through a backpropagation (BP) neural network enhanced by the particle swarm optimization algorithm. The model's accuracy is validated through simulation testing, with the optimized hydrophone sensitivity and effective electromechanical coupling coefficient averaging 99.77% and 96.03%, respectively. A MEMS hydrophone is fabricated based on the design model's optimization results, and experimental testing confirmed the design performance. This method can meet the design requirements of MEMS hydrophones with improved efficiency and precision and can be extended to other MEMS sensors and actuators requiring structural parametric design.

关键词： Echo sounders Micromechanical devices optimization Sensitivity Electrodes Acoustics Resonant frequency MEMS hydrophone particle swarm optimization algorithm sensitivity structural optimization design

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Two-Stage Optimal Scheduling Based on the Meteorological Prediction of a Wind-Solar-Energy Storage System with Demand Response

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ENERGIES 2024年第6期17卷 1286页

作者： Wei, Lu Li, Yiyin Xie, Boyu Xu, Ke Meng, Gaojun Henan Meteorol Serv Ctr Zhengzhou 450003 Peoples R China Nanjing Inst Technol Sch Elect Power Engn Nanjing 211167 Peoples R China

With large-scale wind and solar power connected to the power grid, the randomness and volatility of its output have an increasingly serious adverse impact on power grid dispatching. Aiming at the system peak shaving problem caused by regional large-scale wind power photovoltaic grid connection, a new two-stage optimal scheduling model of wind solar energy storage system considering demand response is proposed. There is a need to comprehensively consider the power generation cost of various types of power sources, day-ahead load forecasting information, and other factors and plan the day-ahead output plan of the energy storage system with the minimum system operation cost as the optimization objective of day-ahead dispatching. The demand response strategy is introduced into the time-ahead optimal scheduling, and the optimization of the output value of the energy storage system in each period is studied with the goal of minimizing the system adjustment cost. The particle swarm optimization algorithm is used to solve the model, and the IEEE33 node system is used for an example simulation. The results show that using the demand response and the collaborative effect of the energy storage system can suppress the uncertainty of wind power and photovoltaic power, improve the utilization rate of the system, reduce the power generation cost of the system, and achieve significant comprehensive benefits.

关键词： demand response energy storage system two-stage scheduling particle swarm optimization algorithm

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