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IEEE International Conference on Mechatronics Automation

作者： Wang, YuShen Wang, Kejun Qu, JiaSheng Yang, YuRong Harbin Engn Univ Coll Automat Harbin 150001 Peoples R China

ISBN: (纸本)078039044X

Two off-line neural networks were trained by applying particle swarm optimization algorithm to create object model and object inverse model of model reference adaptive inverse control system. The method and procedure in training the network of control system was given by using particle swarm. Double inverted pendulum system was used for research object in simulation. The result of experiment proved that this algorithm can obtain more stability performance, and easy to achieve.

关键词： particle swarm optimization algorithm neural network adaptive inverse control inverted pendulum

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Design of Electric Carbon Emission Assessment System Based on particle swarm optimization algorithm 3

Design of Electric Carbon Emission Assessment System Based o...

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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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A Novel Competitive particle swarm optimization algorithm Based on Levy Flight

A Novel Competitive Particle Swarm Optimization Algorithm Ba...

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2019 Chinese Intelligent Systems Conference

作者： Ning, Yao Liu, Zhongxin Chen, Zengqiang Zhao, Chen Nankai Univ Coll Artificial Intelligence Tianjin 300350 Peoples R China Key Lab Intelligent Robot Tianjin Tianjin 300350 Peoples R China

ISBN: (纸本)9789813296848;9789813296824;9789813296817

The particle swarm optimization is a classical optimization algorithm (PSO) that has been applied to various fields. It implements simple but efficient evolution operators to search for optimums in parameter space. However, it could not reach a good balance between global search and local search when it comes to multimodal' problem. Levy flight is a kind of stochastic process with scale invariant, turned out to accord to animals' behavior. This paper utilizes the levy flight operator to enhance the PSO's global search ability during the iteration, which is based on the hierarchy structure of swarms. Therefore, a novel competitive particle swarm optimization based on levy flight (CLFPSO) is proposed. A number of benchmarks has been tested on the CLPSO with other five typical PSO algorithms. The experimental results show that the proposed algorithm could reach more outstanding and accurate consequences compared with other algorithms.

关键词： particle swarm optimization algorithm Hierarchy structure Levy flight A number of benchmarks

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Multi-objective Optimal Tourism Route Planning and Design Based on Improved particle swarm optimization algorithm

Multi-objective Optimal Tourism Route Planning and Design Ba...

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作者： Yan, Jingjing Henan Vocational College of Agriculture Henan Zhengzhou China

In the tourism industry, in order to attract more tourists, there must be a key link, which is route planning. However, the complex optimization problem in tourism route planning has always been a headache. Traditional single route planning often lacks flexibility and personalization and is not conducive to deep tourism. Therefore, this paper proposed a multi-objective optimal tourism route planning and design method based on the improved particle swarm optimization (PSO), which aimed to meet the needs of people for better tourism experience and hoped to further promote the development of tourism. The experimental results showed that this method can not only obtain the optimal solution in tourism routes, but also has advantages in algorithm efficiency and convergence speed. Compared with the previous improvement, the time for solving the optimal route has been reduced by 54%, and the algorithm's efficiency in solving the optimal route has also reached 93%. This showed that the improved PSO can be used as an effective multi-objective optimal tourism route planning method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Improved algorithm Multi-objective optimal design particle swarm optimization algorithm Tourism route planning

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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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Blending Scheduling under Uncertainty Based on particle swarm optimization algorithm

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Chinese Journal of Chemical Engineering 2005年第4期13卷 535-541页

作者： ZHAO Xiaoqiang（赵小强） RONG Gang（荣冈） National Key Laboratory of Industrial Control Technology Institute of Advanced Process Control Zhejiang University Hangzhou 310027 China

Blending is an important unit operation in process industry. Blending scheduling is nonlinear optimiza- tion problem with constraints. It is difficult to obtain optimum solution by other general optimization methods. particle swarm optimization (PSO) algorithm is developed for nonlinear optimization problems with both contin- uous and discrete variables. In order to obtain a global optimum solution quickly, PSO algorithm is applied to solve the problem of blending scheduling under uncertainty. The calculation results based on an example of gasoline blending agree satisfactory with the ideal values, which illustrates that the PSO algorithm is valid and effective in solving the blending scheduling problem.

关键词： blending scheduling uncertainty gasoline blending particle swarm optimization algorithm nonlinear optimization

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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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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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