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Low-Carbon Cold-Chain Logistics Path optimization Problem Considering the Influence of Road Impedance

IEEE ACCESS 2023年 11卷 124055-124067页

作者： Zhang, Aobei Zhang, Ying Liu, Yanqiu Shenyang Univ Technol Sch Management Shenyang 100870 Peoples R China

An improved road impedance function in conformity with Chinese city road traffic is designed for calculating the actual transportation time based on real-time traffic information and the complex road environment in the city. The objective function minimizes the total distribution cost, including fixed cost, transportation cost, damage cost, energy cost, penalty cost, and carbon emission cost. A mathematical model is constructed for cold-chain logistics distribution path optimization considering the influence of road impedance. The model is solved using three particle swarm optimization algorithms with improved weights. The experimental results show that the self-adaptive weighted particle swarm optimization algorithm is more efficient in solving this model. Experimental results obtained from the cold-chain logistics path optimization model considering road impedance compared to those of the model without road impedance indicate that the former are closer to the actual situation. Based on the changes in carbon emission and total cost under different carbon taxes, we analyze the critical carbon tax value and the optimal carbon tax value range to improve the economic and environmental benefits in the distribution process. This study should prove to be of great practical significance and application value for logistics enterprises to conduct rational planning of path problems.

关键词： Real-time traffic road impedance function particle swarm optimization algorithm coldchain logistics critical carbon tax value

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Evaluation of problems and countermeasures in marketing teaching based on cloud computing data aggregation algorithm

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SN APPLIED SCIENCES 2023年第5期5卷 1-12页

作者： Shen, Weinan Suzhou Vocat Univ Sch Management Suzhou 215000 Jiangsu Peoples R China

In this study, we present a highly practical marketing course. Through learning marketing, we can cultivate students' ability to analyze, solve and practice problems. Therefore, this paper would study the problems encountered in the marketing course through cloud computing technology and propose corresponding countermeasures, so as to better improve students' ability to analyze and solve problems and enhance the effectiveness of the classroom. This paper also analyzed the data aggregation algorithm based on particle swarm optimization (PSO) algorithm. The results showed that when other conditions were the same, only 6 students approve of the traditional marketing teaching mode, accounting for 12%;there were 44 people who did not approve, accounting for 88%. This number was more than half;49 students recognized the optimized marketing teaching mode, accounting for 98%;there was 1 person who did not approve, accounting for 2%. The proportion of people who did not approve after optimization was far less than that of the traditional model, which showed that cloud computing data aggregation algorithm could find out the problems in marketing teaching and provide correct countermeasures to solve them, so that the teaching model could be optimized. (1) The data aggregation algorithm based on cloud computing has a good optimization effect on the evaluation of marketing teaching problems and countermeasures, indicating a new direction for future research. (2) The combination of cloud computing and data aggregation algorithm is not only a novel research perspective, but also a perfect idea. (3) Marketing course is a very important course in today's society. The study of its teaching problems and countermeasures evaluation will help students improve their learning efficiency and enhance their ability to adapt to the society.

关键词： Marketing teaching Cloud computing Data aggregation algorithm Analysis of problems and countermeasures particle swarm optimization algorithm

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Fault diagnosis of high power grid wind turbine based on particle swarm optimization BP neural network during COVID-19 epidemic period

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第6期39卷 9027-9035页

作者： Chen, Xi Zhoukou Normal Univ Sch Mech & Elect Engn 9 Wenchang St Chuanhui Dist Zhoukou Peoples R China

During the COVID-19 pandemic, the maintenance of the wind turbine is unable to be processed due to the problem of personnel. This paper presents two neural network models: BP neural network and LSTM neural network combined with particle swarm optimization (PSO) algorithm to realize obstacle maintenance detection for wind turbine Aiming at the problem of gradient vanishing existing in the traditional regression neural network, a fault diagnosis model of wind turbine rolling bearing is proposed by using long-term and short-term memory neural network. Through the analysis of an example, it is verified that the diagnosis results of this method are consistent with the actual fault diagnosis results of wind turbine rolling bearing and the diagnosis accuracy is high. The results show that the proposed method can effectively diagnose the rolling bearing of wind turbine, and the long-term and short-term memory neural network still has good fault diagnosis performance when the difference of fault characteristics is not obvious, which shows the feasibility and effectiveness of the method.

关键词： Wind turbine fault diagnosis particle swarm optimization algorithm BP neural network

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Lyapunov Control of High-Dimensional Closed Quantum Systems Based on particle swarm optimization

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IEEE ACCESS 2020年 8卷 49765-49774页

作者： Guan, Xiaoke Kuang, Sen Lu, Xiujuan Yan, Jiazhen Univ Sci & Technol China Dept Automat Hefei 230027 Peoples R China Univ Sci & Technol China CAS Key Lab Technol Geospatial Informat Proc & Ap Hefei 230027 Peoples R China

For high-dimensional closed quantum systems, this paper proposes a novel quantum Lyapunov control scheme based on the particle swarm optimization algorithm and achieves a high-probability population transfer of the system to a non-isolated target eigenstate in the LaSalle invariant set under usual smooth Lyapunov control laws. Via a quadratic Lyapunov function with unknown parameters, a control law with the unknown parameters is designed;based on the LaSalle invariance principle and the energy-level connectivity graph, the stability of the system is analyzed;by using the particle swarm optimization algorithm, a set of optimal parameters is obtained to achieve the control goal. In particular, we propose a path planning method based on the energy-level connectivity graph to determine the initial values of the unknown parameters, which is such that the optimization algorithm can efficiently and conveniently find a set of optimal solutions of the unknown parameters. Numerical simulation experiments on a five-level quantum system and a three-qubit system demonstrate that the proposed Lyapunov control scheme based on the particle swarm optimization algorithm has a good control effect.

关键词： Lyapunov methods Sociology Statistics particle swarm optimization Control systems Quantum entanglement High-dimensional quantum systems Lyapunov control particle swarm optimization algorithm parameter optimization initial values energy-level connectivity graph

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Design and optimization of power supply communication system for indoor wheeled inspection robots

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年

作者： Su, Renqi Shen, Hongming Huang, Yin Zhao, Shun Huaneng Zhejiang Energy Dev Co Ltd Clean Energy Branch Room 303Huaneng Bldg Hangzhou 310022 Peoples R China Huaneng Zhejiang Energy Dev Co Ltd Hangzhou Peoples R China

The existing power supply and communication system for indoor wheeled patrol robots relies on magnetic coupling mechanisms, but magnetic coupling mechanisms rarely meet the stability requirements of the system. In response to the challenge, a magnetic resonance-based wireless power and signal transmission system for robots is designed in this paper. It proposes transmitter coil switching control technology and receiver continuous energy harvesting technology, and establishes a communication model for one-to-many reception under different system states during motion. Based on this, a mathematical model for optimizing the structure of the magnetic coupling mechanism among transmitter and receiver coils was established. Optimal parameter values were determined using an enhanced particle swarm optimization algorithm (EAPSO). Experimental outcome implies that the transmission efficiency of the proposed method is 87.9%, representing an improvement of 4.8%-20.5%, and it can achieve efficient system stability control.

关键词： indoor wheeled patrol robot power supply and communication system magnetic coupling resonance particle swarm optimization algorithm transmitter and receiver coils

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Mechanism of time-delay feedback control of suspension damping with an annular vibration-absorbing structure

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JOURNAL OF VIBROENGINEERING 2023年第8期25卷 1561-1582页

作者： Chen, Yajie Ma, Kehui Ren, Chuanbo Nan, Yang Zhou, Pengcheng Shandong Univ Technol Sch Transportat & Vehicle Engn Zibo 255000 Peoples R China

With the aim of enhancing both the ride comfort and the safety of the vehicle, we propose a new type of suspension with an annular vibration-absorbing structure, and establish a 3-DOF 1/4 vehicle model. The structure parameters and time-delay feedback control parameters are determined by particle swarm optimization algorithms, which take the root mean values of body acceleration, suspension dynamic deflection, and tire dynamic displacement as their optimization objectives. We analyze the stability of the suspension control system to ensure the stability of the time-delay control system through the Routh-Hurwitz stability criterion, characteristic root method, and stability switching method. Then, we compare and analyze the response characteristics of conventional suspension, new suspension without time-delay feedback control, and new suspension with time-delay feedback control under simple harmonic excitation and random excitation. The results show that the new suspension with time-delay feedback control has a significant damping effect on the body under the premise of ensuring the stability of the system.

关键词： suspension structure vehicle vibration damping time-delay feedback control stability analysis particle swarm optimization algorithm

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Research on Comprehensive Control of Power Quality of Port Distribution Network Considering Large-Scale Access of Shore Power Load

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Energy Engineering 2023年第5期120卷 1185-1201页

作者： Yuqian Qi Mingshui Li Yu Lu Baitong Li Tianjin University Tianjin300110China

In view of the problem of power quality degradation of port distribution network after the large-scale application of shore power load,a method of power quality management of port distribution network is *** on the objective function of the best power quality management effect and the smallest investment cost of the management device,the optimization model of power quality management in the distribution network after the large-scale application of large-capacity shore power is *** on the balance between the economic demand of distribution network resources optimization and power quality management capability,the power quality of distribution network is considered *** proposed optimization algorithm for power quality management based on Matlab and OpenDSS is proposed and analyzed for port distribution *** simulation results show that the proposed optimizationmethod can maximize the power qualitymanagement capability of the port distribution network,and the proposed optimization algorithm has good convergence and global optimization finding capability.

关键词： Shore power harmonic control multi objective optimization particle swarm optimization algorithm

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D-PSO: An optimization algorithm for Wireless Sensor Network Layout in Power Systems 9

D-PSO: An Optimization Algorithm for Wireless Sensor Network...

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9th International Conference on Automation, Control and Robotics Engineering (CACRE)

作者： Ma, Pengwei Zhu, Yuanjiao Sun, Zheng Wei, Jingyi Luo, Yunchen Zhou, Jie Shihezi Univ Coll Informat Sci & Technol Shihezi Peoples R China

ISBN: (纸本)9798350350319;9798350350302

The effective deployment of wireless sensor networks (WSNs) is a crucial foundation for the intelligent development of power systems. To address the optimization of wireless sensor distribution in power systems, this paper proposes a novel method named the Distributed particle swarm optimization algorithm (D-PSO). This method mitigates the premature convergence issue of heuristic algorithms by introducing a regional operator. Additionally, considering the high-interference environment in power systems, relay node strategy (RNS) is incorporated to ensure communication quality. Simulation results validate the effectiveness and superiority of the D-PSO method and demonstrate the necessity of the RNS. Compared to some advanced particle swarm algorithms, this method better balances energy consumption, coverage, and communication quality, thereby significantly enhancing the overall performance of the wireless sensor network.

关键词： wireless sensor network power systems particle swarm optimization algorithm

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Automatic scheduling and control technology of pump gate clusters of regional water conservancy project

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DESALINATION AND WATER TREATMENT 2023年 293卷 82-88页

作者： Sun, Aijun Hu, Donghui Shan, Chengju Wang, Jun Yuyao Water Resources Bur Ningbo 315470 Peoples R China Zhejiang Univ Water Resources & Elect Power Hangzhou 310018 Peoples R China

With the frequent regional flood disasters in recent years, the study of automatic scheduling and control technology of pump gate clusters has become crucial in related academic circles. Here, an automatic flood control and scheduling model for pump and gate clusters of water conservancy projects is constructed by combining hormone regulation and particle swarm algorithm. The model can regulate the maximum discharge flow and maximum water level of reservoirs according to the actual situation in order to achieve flood prevention and consumption prevention. The model is validated using two reservoirs in city A as an example. The results show that the maximum dis-charge flow of the two reservoirs after the model treatment is 1,996.16 and 16,738.28 m3/s, which reduces the incidence of flooding in city A. In addition, after the optimal flood control scheduling, the maximum water levels of the once-in-a-century flood and once-in-two-centuries flood in city A become 80.41 and 81.66 m, respectively, which are in the safe water level range. The study is of great significance for the reduction of flooding.

关键词： Regional water conservancy project Pump gate clusters Hormone regulation particle swarm optimization algorithm Scheduling and control

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Research Review of Ship Power Load Forecasting Based on Intelligent optimization algorithms

Research Review of Ship Power Load Forecasting Based on Inte...

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2024 IEEE International Conference on Cognitive Computing and Complex Data

作者： Meng, Ningjia Mei, Jiahao Shen, Kang Liang, Tianqin Zhang, Yuanyuan Xie, Jijin Beibu Gulf Univ Coll Mech & Marine Engn Qinzhou Peoples R China Beibu Gulf Univ Eastern Michigan Joint Coll Engn Qinzhou Peoples R China

ISBN: (纸本)9798350352900;9798350352894

Amidst the swift advancements of the global shipping industry and the escalating demand for energy conservation and emission reduction, ship power load forecasting has emerged as a pivotal research domain to guarantee the stable operation of ship power systems. Precise power load forecasting not only optimizes the scheduling and management of ship power systems but also markedly enhances fuel utilization efficiency and mitigates emissions. In recent years, intelligent optimization algorithms, renowned for their notable advantages in addressing complex nonlinear problems, have been increasingly applied to the study of ship power load forecasting. This paper delves into ship power load forecasting methodologies predicated on intelligent optimization algorithms, with a primary focus on the application of genetic algorithms, particle swarm optimization algorithms, and artificial neural networks. These algorithms emulate natural evolution, swarm intelligence, and neural system mechanisms, thereby effectively addressing complex power load forecasting challenges. In conclusion, ship power load forecasting founded on intelligent optimization algorithms holds substantial theoretical significance and extensive application prospects. With the advancement and application of related technologies, the operational efficiency and reliability of ship power systems will be further augmented, thereby furnishing robust support for attaining green shipping and sustainable development.

关键词： intelligent optimization algorithms ship power load forecasting genetic algorithm particle swarm optimization algorithm

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