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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Wu, Fangquan Liu, Yichi Tang, Chengjia Hu, Junhan Guizhou Power Grid Guizhou China

ISBN: (纸本)9798350317022

The traditional electric carbon emission assessment model can no longer meet the increasingly complex and changeable needs of enterprises. Based on big data analysis and big data technology, a dynamic planning model suitable for electric carbon emission assessment system is constructed. Then, we describe in detail the construction and solution method of dynamic programming model of electric carbon emission assessment system based on particle swarm optimization algorithm. The model will comprehensively consider key factors through big data algorithms. Finally, we verify the effectiveness and practicality of the model through case studies and empirical analysis. The results show that the dynamic planning model of electric carbon emission assessment system based on particle swarm optimization algorithm can help enterprises predict demand more accurately, allocate reasonably, and improve performance and organizational efficiency. The application of this model can effectively improve the electric carbon emission assessment system efficiency and decision-making ability, thereby enhancing the competitiveness and sustainable development of enterprises. © 2023 IEEE.

关键词： Big Data Decision Support Electric Carbon Emission Assessment System Optimal Configuration particle swarm optimization algorithm

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optimization and Performance Analysis of particle swarm optimization algorithm in Learning Behavior System

Optimization and Performance Analysis of Particle Swarm Opti...

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2023 International Conference on Evolutionary algorithms and Soft Computing Techniques, EASCT 2023

作者： Guo, Miaoxiu Shandong Institute of Commerce and Technology Jinan China

ISBN: (纸本)9798350313413

With the rapid development of intelligent control technology, artificial intelligence has become an indispensable part of people's life. The particle swarm optimization algorithm has incomparable advantages. It obtains the optimal solution through information exchange between groups. In order to strengthen the learning efficiency of college students and improve their abilities, this paper uses the particle swarm algorithm to calculate the learning behavior of college students, and designs and optimizes the analysis module, strategy module and model of the system. In this paper, a genetic optimization model based on particle swarm optimization algorithm is established, and it is designed and simulated in a computer environment. For the iterative method, performance analysis and parameter estimation are proposed, and the entire learning process can be completed efficiently by continuously adjusting its own internal parameters. And by means of experimental analysis and algorithm performance comparison, the ability of particle swarm optimization algorithm is obtained. The experimental results show that the effectiveness of the improved multi-objective optimization particle swarm optimization algorithm in the learning behavior analysis system reaches 93%, and it is the most effective algorithm among the four algorithms. Therefore, in this system, a variety of particle swarm optimization algorithms can be combined to make full use of the MOPSO algorithm to improve system performance. © 2023 IEEE.

关键词： learning behavior optimization system particle swarm optimization algorithm performance analysis

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Automatic Pricing and Replenishment Strategy for Vegetable Products Based on ARIMA Prediction and particle swarm optimization algorithm

Automatic Pricing and Replenishment Strategy for Vegetable P...

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2023 IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Xiao, Han Chen, Lijia Dai, Zhiyi Wenhua College Wuhan China

ISBN: (纸本)9798350309614

In fresh food supermarkets, the freshness period of vegetable products is very important, in order to ensure the quality and freshness of the goods, supermarkets need to focus on the daily sales situation to carry out timely replenishment operations. This paper assists merchants to make the best replenishment and pricing strategy to maximize the benefits. First of all, this paper analyzes the correlation and distribution law between each vegetable category and single product according to the sales data of a superstore over the past three years. And then this paper analyzes the relationship between sales volume and cost plus pricing, establishes the price elasticity of demand model, and combines it with the ARIMA model to establish the automatic pricing and replenishment optimization model for vegetables. Finally, we use the optimization algorithm to get the optimal pricing and replenishment strategy. © 2023 IEEE.

关键词： ARIMA Model Automatic Pricing Replenishment Strategy Correlation particle swarm optimization algorithm

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Design of decision evaluation model for the infrastructure management system based on particle swarm optimization algorithm

Design of decision evaluation model for the infrastructure m...

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2024 International Conference on Computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2024

作者： Zheng, Yinglong Huang, Binxi Li, Baohua Fan, Xuan Pan, Xiaoen CSG Digital Grid Group Co. Ltd. Guangdong Branch Guangdong Guangzhou 510030 China

ISBN: (纸本)9781510689275

In complex decision-making environments, traditional decision-making methods may not be able to find optimal or satisfactory solutions effectively. Therefore, intelligent optimization algorithms are needed to assist decision-making in order to improve the efficiency and effectiveness of decision-making. Therefore, this paper proposes the design of decision-making evaluation model for the infrastructure management system based on particle swarm optimization algorithm, which uses intelligent optimization algorithms to assist infrastructure project management and decision-making methods. Firstly, the uncertainty factors affecting the decision-making of the infrastructure management system are analyzed, and the rolling window strategy is used to divide the service time of the demand order into multiple time slices of equal length, and then the particle swarm optimization algorithm-based infrastructure management system decision evaluation model design is used to achieve real-time optimization of multiple objectives within each rolling window. The experimental results show that in infrastructure projects, the rational allocation of resources is crucial to the success of the project. PSO can help decision makers to find the best resource allocation solution among multiple available options to maximise costs and benefits. The PSO algorithm is able to deal with complex non-linear problems and take into account the trade-offs of multiple decision objectives. This makes the decision-making results more scientific and comprehensive, and helps to improve the overall decision-making quality of infrastructure management. © 2025 SPIE.

关键词： Consistency Decision evaluation model Infrastructure management system particle swarm optimization algorithm

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Application Evaluation of particle swarm optimization algorithm in Organic Pollution Control Prediction System 1

Application Evaluation of Particle Swarm Optimization Algori...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： He, Zixin University of Washington Seattle United States

ISBN: (纸本)9798350316469

Pollution control is a complex system engineering, and in order to truly achieve comprehensive environmental management and protection, it is necessary to do a good job in controlling pollution at the source. However, due to historical reasons and economic development levels, many places still have serious pollution phenomena. This paper mainly analyzes and discusses the problem of organic pollution, uses particle swarm optimization (PSO) algorithm to screen pollutants, and combines BP (back propagation) neural network to establish a pollution source prediction system. According to the selected optimal solution, a new mathematical model is constructed, which can better serve environmental protection, improve work efficiency, and reduce operating costs, It has high practical value. Comparing the PSO algorithm model with the BP neural network model in the treatment of organic pollutants, experimental data shows that the application effect of the PSO algorithm is more ideal, the prediction results are more accurate, and the pollutant removal rate has also increased by about 3.53%, providing new ideas and methods for environmental science and engineering exploration. The research results show that applying PSO algorithm to organic pollution control prediction systems can achieve certain results. By optimizing model parameters or control strategy parameters, the accuracy and control effectiveness of organic pollutant prediction can be improved. The PSO algorithm can also be used to optimize risk assessment models and improve the accuracy of organic pollutant risk assessment. © 2023 IEEE.

关键词： BP Neural Network Management Method Organizational Polling Control particle swarm optimization algorithm Prediction System

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Research on Optimal Allocation of Distributed Power Supply Based on particle swarm optimization algorithm 3

Research on Optimal Allocation of Distributed Power Supply B...

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3rd International Conference on New Energy and Power Engineering, ICNEPE 2023

作者： Huang, Guanglei Zhou, Baorong Mao, Tian Jiang, Jiru Wang, Tao Zhao, Wenmeng Chen, Shuyao China Southern Power Grid Shenzhen Power Supply Company Shenzhen518067 China China Southern Power Grid Electric Power Research Institute Guangzhou510530 China

ISBN: (纸本)9798350393224

The distribution network is connected to a multitude of dispersed power sources and electric car clusters, which results in a diverse range of load types. The distribution network planning is greatly challenged by dispersed generation's randomness and instability. In order to adjust to the distribution network, the planning method of varied load access is therefore suggested. In order to maximize the capacity of dispersed generation permitted to access the line, the distribution network planning model is first constructed. Then, a variety of alternate strategies are produced by gaining access to the distributed power supply at every node;In order to direct the distribution network's planning, the best access strategy for the created enormous access scheme is ultimately chosen using the particle swarm optimization (PSO) technique. The model's rationality and validity are confirmed by the simulation results of real-world situations. © 2023 IEEE.

关键词： capacity configuration dispersed power supply distribution network planning particle swarm optimization algorithm

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Residual Vibration Suppression of Piezoelectric Inkjet Printing Based on particle swarm optimization algorithm

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MICROMACHINES 2024年第10期15卷 1192-1192页

作者： Zhu, Huixuan Li, Song Zhu, Runyang Gao, Feiyang Yin, Zhenyu Liu, Lianqing Zheng, Xiongfei Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Shenyang Inst Comp Technol Shenyang 110168 Peoples R China Liaoning Key Lab Domest Ind Control Platform Techn Shenyang 110168 Peoples R China

Piezoelectric inkjet printing technology, known for its high precision and cost-effectiveness, has found extensive applications in various fields. However, the issue of residual vibration significantly limits its printing quality and efficiency. This paper presents a method for suppressing residual vibration based on the particle swarm optimization (PSO) algorithm. Initially, an improved PI model considering the nonlinear hysteresis characteristics of piezoelectric ceramics is established, and the model is identified through a strain gauge circuit to ensure its accuracy in describing the nonlinear hysteresis characteristics. Subsequently, a dynamic model of the piezoelectric inkjet printing system is constructed, with precise parameter identification achieved using the self-induction principle. This enables precise simulation of residual vibration. Finally, the driving waveform is optimized based on the PSO algorithm, with iterative calculations employed to find the optimal combination of driving waveform parameters, effectively suppressing residual vibration while ensuring sufficient injection energy. The results indicate that this method significantly reduces the amplitude of residual vibration, thereby effectively enhancing printing quality and stability. This research offers a novel solution for residual vibration suppression in piezoelectric inkjet printing technology, potentially advancing its applications in printing and biofabrication.

关键词： piezoelectric inkjet printing residual vibration suppression nonlinear hysteresis characteristics particle swarm optimization algorithm self-induction principle

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Convergence and Performance Analysis of a particle swarm optimization algorithm for Optical Tuning of Gold Nanohole Arrays

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MATERIALS 2024年第4期17卷 807页

作者： Angelini, Margherita Zagaglia, Luca Marabelli, Franco Floris, Francesco Univ Pavia Dept Phys Via Bassi 6 I-27100 Pavia Italy

Gold nanohole arrays, hybrid metal/dielectric metasurfaces composed of periodically arranged air holes in a thick gold film, exhibit versatile support for both localized and propagating surface plasmons. Leveraging their capabilities, particularly in surface plasmon resonance-oriented applications, demands precise optical tuning. In this study, a customized particle swarm optimization algorithm, implemented in Ansys Lumerical FDTD, was employed to optically tune gold nanohole arrays treated as bidimensional gratings following the Bragg condition. Both square and triangular array dispositions were considered. Convergence and evolution of the particle swarm optimization algorithm were studied, and a mathematical model was developed to interpret its outcomes.

关键词： FDTD simulation particle swarm optimization algorithm gold nanohole arrays

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Joint Inversion Method for Ionospheric Properties Based on particle swarm optimization algorithm 23

Joint Inversion Method for Ionospheric Properties Based on P...

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Yang, Yangqiang Wang, Menglin Zhang, Xueqing College of Information and Communication National University of Defense Technology Wuhan China

ISBN: (纸本)9798350325959

The ionosphere with charged particles such as free electrons and positive ions, is one of the most important factors affecting the accuracy of satellite navigation system, the quality of short-wave communication and the safety of power transmission. The detection and inversion methods of the ionosphere have always been the focus of ionospheric research. Obtaining data through various ionospheric detection systems and then using corresponding inversion models and algorithms is an important method to obtain ionospheric properties. By comprehensively using ionospheric altimeter data, typical regional TEC data, and IRI empirical model data, this paper proposed a joint ionospheric property inversion method based on particle swarm optimization algorithm to overcome the large errors that may be caused by using single source data. Simulation results showed that the proposed method can be used for ionospheric properties inversion. © 2023 IEEE.

关键词： ionospheric inversion IRI model particle swarm optimization algorithm QPS model TEC data

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Kinematic Parameter Identification for a Parallel Robot with an Improved particle swarm optimization algorithm

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APPLIED SCIENCES-BASEL 2024年第15期14卷 6557页

作者： Yu, Dayong Univ Shanghai Sci & Technol Sch Mech Engn Shanghai 200093 Peoples R China

The spacecraft docking motion simulation system for on-orbit docking plays a very important role in some theoretical research and engineering application fields. The parallel robot utilized in the spacecraft docking simulation system requires high positioning and orientation accuracy to achieve better simulation results. A novel kinematic parameter identification method with an improved particle swarm optimization (PSO) algorithm is proposed to enhance positioning and orientation accuracy of the parallel robot. A fitness function is established using these residuals between the measured and computed poses by a coordinate measuring machine and forward kinematics. The kinematic parameter identification problem is turned into a high-dimensional nonlinear optimization in which the unknown kinematic parameter errors are regarded as optimal variables. The optimal variables are solved by the proposed improved PSO algorithm. The mean values of the positioning and orientation errors are reduced from 4.3268 mm and 0.2221 deg to 0.7692 mm and 0.0674 deg, respectively. The proposed kinematic parameter identification method increases the positioning accuracy mean by 22.26% and the orientation accuracy mean by 32.80% compared with the least squares method. The kinematic parameter identification method with the improved PSO algorithm can effectively enhance positioning and orientation accuracy of the parallel robot for docking motion simulation.

关键词： parallel robot parameter identification particle swarm optimization algorithm pose accuracy nonlinear least squares

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