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36th Chinese Control and Decision Conference (CCDC)

作者： Luo, Yuzhu Yu, Xiaoyu Zhou, Yue Bai, Bing Dong, Huan Peng, Xiuyuan Liaoning Acad Agr Sci Inst Informat Shenyang Peoples R China Liaoning Acad Agr Sci Inst Plant Nutr & Environm Resources Shenyang Peoples R China

ISBN: (纸本)9798350387780;9798350387797

Nutrient management is a key measure to achieve the target crop yield. Choosing appropriate predictive models and adjusting nutrient supply according to actual conditions can effectively reduce nutrient loss, improve crop yield and product quality. Therefore, we conducted research on the prediction models of inorganic fertilizers N, P, and K in greenhouse tomatoes based on the target yield. In this paper, we analyzed four different neural network prediction models: the neural network prediction model based on ant, colony optimization algorithm, the neural network prediction model based on sparrow search algorithm, neural network prediction model based on genetic algorithm and neural network prediction model based on particle swarm optimization algorithm, and compared and analyzed the prediction results of inorganic fertilizer N, P and K under different soil fertility conditions to understand which neural network prediction model will produce the best prediction effect. The simulation results showed that under medium soil fertility, the neural network prediction model based on genetic algorithm had the best prediction effect of inorganic fertilizer N, P, and K. The verification results showed that its mean square error (MSE) and coefficient of determination (R-2) were the best, which were 0.0031 and 0.8200 respectively. However, under low soil fertility and high soil fertility, the neural network prediction model based on the sparrow search algorithm had the best prediction performance, and its MSE and R-2 were the best.

关键词： ant colony optimization algorithm sparrow search algorithm genetic algorithm particle swarm optimization algorithm inorganic fertilizer prediction

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Equilibrium customer and socially optimal balking strategies in a constant retrial queue with multiple vacations and N-policy

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JOURNAL OF COMBINATORIAL optimization 2022年第4期43卷 870-908页

作者： Wang, Zhen Liu, Liwei Zhao, Yiqiang Q. Guilin Univ Elect Technol Sch Math & Comp Sci Guilin 541004 Guangxi Peoples R China Nanjing Univ Sci & Technol Sch Sci Nanjing 210094 Jiangsu Peoples R China Carleton Univ Sch Math & Stat 1125 Colonel Dr Ottawa ON K1S 5B6 Canada

In this paper, equilibrium strategies and optimal balking strategies of customers in a constant retrial queue with multiple vacations and the N-policy under two information levels, respectively, are investigated. We assume that there is no waiting area in front of the server and an arriving customer is served immediately if the server is idle;otherwise (the server is either busy or on a vacation) it has to leave the system to join a virtual retrial orbit waiting for retrials according to the FCFS rules. After a service completion, if the system is not empty, the server becomes idle, available for serving the next customer, either a new arrival or a retried customer from the virtual retrial orbit;otherwise (if the system is empty), the server starts a vacation. Upon the completion of a vacation, the server is reactivated only if it finds at least N customers in the virtual orbit;otherwise, the server continues another vacation. We study this model at two levels of information, respectively. For each level of information, we obtain both equilibrium and optimal balking strategies of customers, and make corresponding numerical comparisons. Through particle swarm optimization (PSO) algorithm, we explore the impact of parameters on the equilibrium and social optimal thresholds, and obtain the trend in changes, as a function of system parameters, for the optimal social welfare, which provides guiding significance for social planners. Finally, by comparing the social welfare under two information levels, we find that whether the system information should be disclosed to customers depends on how to maintain the growth of social welfare.

关键词： Multiple vacations Equilibrium strategies Balking strategies particle swarm optimization algorithm Information accuracy

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An Improved Mathematical Model for Estimating Polarization Parameters of Transformer Oil-Paper Insulation

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2022年第4期17卷 2517-2525页

作者： Zhang, Tao Mandala, Antonio Tome Zuo, Qian Zhang, Ning Jiang, Shijie China Three Gorges Univ Coll Elect Engn & New Energy Yichang Peoples R China Xiaogan Power Supply Co State Grid Hubei Elect Power Co Xiaogan Peoples R China

The parameters of dielectric response based on extended Debye circuit model can reflect oil-paper insulation state. The model using the initial slop of recovery voltage to estimate circuit parameters, serve as good quality consistency with the measured data. However, the model can be applicable for large power transformers but not for distribution transformers. This paper proposed an improved mathematical model using recovery voltage peak, peak time and initial slope characteristics to solve dielectric response equivalent circuit parameters. The identification of equivalent circuit parameters is converted into a mathematical optimization problem. And then particle swarm optimization algorithm is used for solving the problem. The improved mathematical model can decrease sampling data of recovery voltage. To check the validity of the estimated parameters, the on-site measured data of RVM experiments on actual transformers in various capacities is applied. The calculated result shows that the recovery voltage curve calculated and measured recovery voltage curve have good consistency, which can be advantageous to diagnose the oil-paper insulation status of transformer. It illustrates that the improved method proposed in this paper is feasible and effective.

关键词： Transformer Oil-paper insulation Recovery voltage method Improved mathematical model particle swarm optimization algorithm

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A Novel Model Based on PSO-GA-RBF Approach for Predicting Roll Gap During Unsteady Processes 3

A Novel Model Based on PSO-GA-RBF Approach for Predicting Ro...

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3rd IEEE International Conference on Electrical Engineering, Big Data and algorithms, EEBDA 2024

作者： Zhang, Ruixiao Mao, Chaoxing Hu, Jinkai Liu, Zhao Guo, Hengda North China University of Science and Technology Tangshan China

ISBN: (纸本)9798350380989

To enhance the control accuracy of strip thickness in the non-stationary process of cold continuous rolling, a PSO-GA-RBF based prediction model for the non-stationary roll gap is proposed. This model combines big data algorithms with actual production data, including roll gap, rolling force, and rolling speed. A 1450mm series cold rolling production line is selected as the research object. Pearson analysis is utilized to perform a correlation analysis on the original data indicators, while principal component analysis is employed to reduce the dimensionality of the sample data. The optimization process of particle swarm optimization is enhanced by introducing crossover and mutation operators from genetic algorithms. This optimization aims to improve the parameter selection mechanism of the radial basis function neural network, thereby enhancing the model's prediction accuracy and generalization performance. The performance of different prediction models is analyzed, comparing them based on evaluation indicators such as mean square error, average absolute error, and average absolute error percentage. The results indicate that the PSO-GA-RBF based roll gap prediction model demonstrates good predictive ability and generalization performance. To further validate the effectiveness of the prediction model, a comparison is drawn between the prediction model and the roll gap data obtained from engineering practice. The experimental results show a close correspondence in trend and period between the two, verifying the strong prediction performance of the roll gap prediction model. The validated model offers benefits such as reducing the workload of the automatic control system and improving the control accuracy of strip steel thickness. © 2024 IEEE.

关键词： Genetic algorithm particle swarm optimization algorithm Radial basis function neural network Roll gap model Thickness control

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ARAZ: A software modules clustering method using the combination of particle swarm optimization and genetic algorithms

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2020年第4期14卷 449-462页

作者： Arasteh, Bahman Sadegi, Razieh Arasteh, Keyvan Islamic Azad Univ Tabriz Branch Dept Comp Engn Tabriz Iran

A considerable percentage of software costs are usually related to its maintenance. Program comprehension is a prerequisite of the software maintenance and a considerable time of maintainers is spent to comprehend the structure and behavior of the software when the source code is the only product available. Program comprehension is one of difficult and challenging task especially in the absence of design documents of the software system. Clustering of software modules is an effective reverse-engineering method for extracting the software architecture and structural model from the source code. Finding the best clustering is considered to be a multi-objective NP hard optimization-problem and different meta-heuristic algorithms have been used for solving this problem. Local optimum, insufficient quality, insufficient performance and insufficient stability are the main shortcomings of the previous methods. Attaining higher values for software clustering quality, attaining higher success rate in clustering of software modules, attaining higher stability of the obtained results and attaining the higher convergence (speed) to generate optimal clusters are the main goals of this study. In this study, a hybrid meta heuristic method (ARAZ) includes particle swarm optimization algorithm and genetic algorithm (PSO-GA) is proposed to find the best clustering of software modules. An extensive series of experiments on 10 standard benchmark programs have been conducted. Regarding the results of experiments, the proposed method outperforms the other methods in terms of clustering quality, stability, success rate and convergence speed.

关键词： Software maintenance software module clustering particle swarm optimization algorithm clustering quality convergence speed

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Optimizing Future Predictions in Children's Health: Implementing OGPA-enhanced Deep Learning for Precise Child Height Forecasting in the Social Media Age 27

Optimizing Future Predictions in Children's Health: Implemen...

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27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Shao, Yantao He, Tianxiang Mao, Yan Fang, Kai Wang, Wei Mao, Keji Zhejiang Univ Technol Sch Comp Sci & Technol Hangzhou Peoples R China Zhejiang A&F Univ Sch Math & Comp Sci Hangzhou Peoples R China Shenzhen MSU BIT Univ AI Res Inst Guangdong HK Macao Jt Lab Emot Intell & Pervas Co Shenzhen Peoples R China

ISBN: (纸本)9798350349184;9798350349191

In recent years, the issue of children's height has garnered widespread attention on social media. Social media platforms serve as pivotal communication channels for parents, doctors, educators, and researchers, with children's height, a crucial health indicator, becoming one of the hot topics. Inaccurate prediction methods might mislead the public, resulting in parents harboring erroneous expectations regarding their children's future height. Such misplaced expectations may culminate in unwarranted worries or pressure. In pursuit of devising an accurate height prediction model, this paper thoroughly leverages a substantial data sample obtained from the physical health examinations of primary and secondary school students in Zhejiang Province, as well as continuous observation samples provided by the Zhejiang Provincial Bone Age Research Center, to delve deeply into the issue of height prediction in children and adolescents. In this research, we have developed a lightweight neural net-work model suitable for particle swarm optimization to predict children's stage-wise height. When the difference between the actual and predicted values is within +/- 2cm, the prediction accuracy for boys reached 86.67%, and for girls, it was 85.32%, with an RMSE of 1.3503.

关键词： Social Media Height Prediction Clustering algorithm Neural Network particle swarm optimization algorithm

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Research on the Reserve Market Adapted to the New Power System Considering Strategic Quotations of Market Participants 2

Research on the Reserve Market Adapted to the New Power Syst...

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IEEE 2nd International Conference on Power Science and Technology (ICPST)

作者： Fan, Menghua Lyu, Chen Zhang, Xiangyu He, Xiran Jing, Zhaoxia State Grid Energy Res Inst Beijing Peoples R China State Grid Shanxi Econ Res Inst Taiyuan Peoples R China South China Univ Technol Sch Elect Power Guangzhou Peoples R China

ISBN: (纸本)9798350349047;9798350349030

With a high proportion of renewable resources connected to the system, sufficient flexible regulation capacity is a necessary condition to ensure the safe and economic operation of the power system. There is an urgent need to study and design a market mechanism for reserve service to motivate flexible resources to actively participate in regulation and promote renewable energy consumption. For the reserve ancillary service, existing studies have focused on the identification of market capacity demand and the construction of market clearing models. However, fewer studies have discussed the efficiency of the combined energy-reserve market under different market competition structures from the point of view of allowing market participants to make strategic offers. It is difficult to effectively analyze the impact of the establishment of a reserve market and its coupled clearing with the energy market on the pre-existing market in a real situation. Thus, this paper identifies a joint clearing mechanism for the energy-reserve market that takes into account the participation of demand-side resources such as interruptible loads. A bi-level model with strategic decision-making by market entities at the upper level and clearing transactions by different market mechanisms at the lower level is also established. Subsequent solving by the particle swarm optimization algorithm can obtain accurate market equilibrium results. This paper is based on the IEEE-30 node system for case analysis. To cope with the entities' games, two scenarios of oligopolistic and perfect competition are considered for simulation, and the bidding behavior of the entities and the total system procurement cost are analyzed under both the current domestic mechanism and the joint energy-reserve market, to verify the validity of the identified market mechanism. The conclusions provide a reference for the construction of a reserve market mechanism that supports the development of flexible regulation resources

关键词： reserve market flexibly regulated resources bi-level model particle swarm optimization algorithm renewable energy consumption

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Generic layout optimization design methodology for China's loop-star natural gas field pipeline network

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NATURAL GAS INDUSTRY B 2024年第5期11卷 616-629页

作者： Chen, Shuangqing Liu, Tianqing Dong, Hongli Bao, Yunbo Yu, Ze Guan, Bing Jiang, Xiaoyu Zhou, Shun Wang, Shanlong Northeast Petr Univ Sch Petr Engn Daqing 163318 Peoples R China Daqing Oilfield Co Ltd Postdoctoral Program Daqing 163000 Peoples R China Second Oil Prod Plant Daqing Oilfield Co Ltd Daqing 163000 Peoples R China Northeast Petr Univ Artificial Intelligence Energy Res Inst Daqing 163319 Peoples R China Daqing Yushulin Oilfield Dev Co Ltd Daqing 163000 Peoples R China Northeast Petr Univ Inst Unconvent Oil & Gas Daqing 163319 Peoples R China

The layout optimization design of a natural gas gathering pipeline network is a multi-objective optimization problem because the extant theories are unable to meet the different decision preferences in scheme design, which restricts the intelligentization of gas gathering pipeline layout optimization. Currently, there are no generic design studies on the loop-star pipeline network. Therefore, this paper proposes a generic layout optimization model containing a large number of discrete and continuous variables, such as pipe connection relationships, pipe sizes, pipe length, and pipe specifications. In the solution section, drawing inspiration from the hormone regulation mechanism and local foraging rule in bionics, an improved particle swarm optimization algorithm based on hormone regulation (HRPSO) is proposed, and it obtains the favorable parameters range of the HRPSO algorithm. The results illustrate that the HRPSO algorithm exhibits convergence to the global optimum with a probability of 1. In comparison to manual design, the comprehensive costs of the optimized scheme are saved by 22.71% with the HRPSO algorithm. Compared to the four PSO variants in the paper, it can save costs by 5.38%, 4.95%, 4.09%, and 3.65%, respectively. (c) 2024 Sichuan Petroleum Administration. Publishing services by Elsevier B.V. on behalf of KeAi Communication Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Natural gas gathering Generic layout optimization model particle swarm optimization algorithm Convergence Comprehensive cost

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Intelligent meta-model construction and global stochastic sensitivity analysis based on PSO-CNN

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STRUCTURES 2022年 43卷 1516-1529页

作者： Dong, Xian Wang, Yadi Wang, Zhan Shijiazhuang Tiedao Univ Sch Civil Engn Shijiazhuang 050043 Hebei Peoples R China South China Univ Technol Dept Civil Engn Guangzhou 510640 Guangdong Peoples R China

A large-scale simulation model is needed for the stochastic sensitivity analysis of structural uncertainty. The finite element method often exceeds the computing power and cannot meet the analysis requirements and computational efficiency of stochastic sensitivity analysis. With the development of artificial intelligence, the calculation efficiency problem can be solved by a combination of the finite element model and a mathematical model, but the accuracy of the analysis results depends on the accuracy of the mathematical model. However, using artificial neural networks to construct mathematical models also has numerous problems, such as falling into local extrema. Thus, to address the computational efficiency and calculation accuracy problems, the particle swarm optimization (PSO)-convolutional neural network (CNN) meta-model is presented in this paper. This model was used to improve the training and prediction accuracy of a small-sample CNN, where the initial learning rate of the CNN was optimized by PSO. Additionally, the PSO was combined with the Adam optimizer to automatically screen the optimal network structure. The global stochastic sensitivity analysis method based on the Sobol sensitivity method and the Monte Carlo method was used to quantify the degree of influence of the input parameters on the model output parameters and to analyze the influence trend of the parameter changes on the structural response combined with the positive and negative Spearman's rank correlation coefficients. The accuracy, good adaptability, and extension ability of the PSO-CNN meta-model was verified using the high -dimensional function examples and the structural models. Global stochastic sensitivity analysis was carried out on the joint of an extended end plate. The influence degree of the parameters on the initial rotational stiffness of the joint was determined, and corresponding design suggestions were obtained. These suggestions were consistent with the design specificati

关键词： Global stochastic sensitivity Convolutional neural network particle swarm optimization algorithm Sobol algorithm Artificial intelligence

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Parameter identification of direct-drive permanent magnet synchronous generator based on EDMPSO-EKF

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IET RENEWABLE POWER GENERATION 2022年第5期16卷 1073-1086页

作者： Xiao, Qianghui Liao, Kaixian Shi, Chuandong Zhang, Yang Hunan Univ Technol Coll Elect & Informat Engn Zhuzhou Hunan Peoples R China

Aiming at the problem that extended Kalman filter (EKF) is difficult to determine the appropriate system and measurement noise matrices in parameter identification of direct-drive permanent magnet synchronous generator (D-PMSG), a parameter identification method of D-PMSG based on mean particle swarm optimization with extreme disturbance (EDMPSO)-EKF is proposed. Firstly, by analysing the principle of EKF, a dual-thread identification model is established, then the particle swarm optimization (PSO) algorithm is improved to jump out of the local optimum by adding extremum interference and taking average extremum, and a suitable fitness function is designed. Finally, the improved PSO algorithm is applied to the adaptive optimization of EKF system noise matrix and measurement noise matrix, and the system performs the parameter identification after obtaining the optimal noise matrix. The simulation and experiment results show that the proposed method has better convergence speed and can more accurately identify the parameters of stator resistance, inductance and flux linkage, and it has better identification accuracy and generalization ability than the traditional method.

关键词： measurement noise matrix extremum interference stators power generation control improved PSO algorithm taking average extremum appropriate system dual-thread identification model measurement noise matrices machine control adaptive optimization particle swarm optimisation permanent magnet generators mean particle swarm optimization parameter estimation synchronous generators Kalman filters identification accuracy particle swarm optimization algorithm direct-drive permanent magnet synchronous generator optimal noise matrix extreme disturbance-EKF generalization ability parameter identification method EDMPSO-EKF nonlinear filters

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