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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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Model and Optimize the Magnetic Composite Fluid (MCF) Polishing Process with Machine Learning Modeling and Intelligent optimization algorithm

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING 2022年第9期23卷 957-967页

作者： Fan, Jinwei Ren, Xingfei Pan, Ri Wang, Peitong Tao, Haohao Beijing Univ Technol Fac Mat & Mfg Beijing 100124 Peoples R China

In the magnetic composite fluid (MCF) polishing process, appropriate polishing parameters are the basis of achieving high-quality polishing without damage. Appropriate polishing parameters are mainly based on an accurate polishing model and an excellent polishing parameters optimization algorithm. However, due to the complicated principle of MCF polishing and various influencing elements, traditional modeling methods have the limitations of low accuracy, poor application, and difficulty in correcting. Therefore, it is challenging to obtain the optimal polishing quality by optimizing the polishing parameters based on the traditional model. This study proposed an online modeling approach considering data cleaning based on machine learning modeling, and the particle swarm optimization (PSO) algorithm was used to optimize polishing parameters. Then, copper polishing experiments were carried out to validate the modeling and optimization methods. The results demonstrate that the proposed machine learning online modeling method can establish an accurate MCF polishing model, and the nano-scale fine polishing of copper can be achieved by the optimized polishing parameters of PSO, and the surface roughness of the copper sample was reduced by 85% to 0.031 mu m.

关键词： Magnetic composite fluid polishing particle swarm optimization algorithm Polishing parameters Surface roughness

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Parameter tuning of auto disturbance rejection controller based on improved glowworm swarm optimization algorithm

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ASSEMBLY AUTOMATION 2022年第4期42卷 427-444页

作者： Gao, Bingwei Shen, Wei Dai, Ye Ye, Yong Tai Harbin Univ Sci & Technol Sch Mech & Power Engn Harbin Peoples R China Harbin Univ Sci & Technol Key Lab Adv Mfg & Intelligent Technol Minist Educ Harbin Peoples R China

Purpose This paper aims to study a parameter tuning method for the active disturbance rejection control (ADRC) to improve the anti-interference ability and position tracking of the performance of the servo system, and to ensure the stability and accuracy of practical applications. Design/methodology/approach This study proposes a parameter self-tuning method for ADRC based on an improved glowworm swarm optimization algorithm. The algorithm is improved by using sine and cosine local optimization operators and an adaptive mutation strategy. The improved algorithm is then used for parameter tuning of the ADRC to improve the anti-interference ability of the control system and ensure the accuracy of the controller parameters. Findings The authors designed an optimization model based on MATLAB, selected examples of simulation and experimental research and compared it with the standard glowworm swarm optimization algorithm, particle swarm algorithm and artificial bee colony algorithm. The results show that the response time of using the improved glowworm swarm optimization algorithm to optimize the auto-disturbance rejection control is short;there is no overshoot;the tracking process is relatively stable;the anti-interference ability is strong;and the optimization effect is better. Originality/value The innovation of this study is to improve the glowworm swarm optimization algorithm, propose a sine and cosine, local optimization operator, expand the firefly search space and introduce a new adaptive mutation strategy to adaptively adjust the mutation probability based on the fitness value, improve the global search ability of the algorithm and use the improved algorithm to adjust the parameters of the active disturbance rejection controller.

关键词： Auto-disturbance rejection controller Glowworm swarm optimization algorithm Parameter tuning particle swarm optimization algorithm External disturbance

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optimization of the operation plan taking into account the flexible resource scheduling of the integrated energy system

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ENERGY REPORTS 2022年 8卷 1752-1762页

作者： Jiang, Xiu-mei Li, Qi-fen Yang, Yong-wen Zhang, Li-ting Liu, Xiao-jing Ning, Ning Shanghai Univ Elect Power Coll Energy & Mech Engn Shanghai 200090 Peoples R China

Under the high proportion of renewable energy access in the integrated energy system, the insufficient total allocation of flexible resources and the unreasonable spatial allocation of resources are important causes of wind abandonment and load shedding. The fundamental solution to this problem is to increase the flexibility of the system to meet demand, so it is important to study multiple types of flexible resources, the state of supply and demand for system flexibility and the optimization of flexibility resource scheduling. This paper studies the flexibility of the integrated energy system. First, it analyzes the basic characteristics of the flexible resources of the system and gives the basic structure of the integrated energy system;Then, the typical flexible resources are modeled and analyzed. Based on the time-of-use price, the minimum daily operation cost of the system is optimized as the objective function, and the particle swarm optimization algorithm is introduced to solve it, so as to explore the flexible adjustment of the system with the participation of flexible resources;Finally, the model established in this paper is used to verify the rationality of the scheduling scheme, and the relationship between the access effect of flexible resources and the location of access nodes is explored. The research on the modeling of integrated energy system and system flexibility in this paper not only provides a tool to reflect the fluctuation of system supply and demand state, but also provides an important basis for system reasonable scheduling, ensuring energy supply quality and improving operation flexibility. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under theCCBY-NC-ND license (http://***/licenses/by-nc-nd/4.0/). Peer-reviewunder responsibility of the scientific committee of the 2021 International Conference on NewEnergy and Power Engineering, ICNEPE, 2021.

关键词： Integrated energy system Flexibility resources System flexibility particle swarm optimization algorithm

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An algorithm with harmonious blending of distributed swarm intelligence and geometric Brownian motion for greener heterogeneous scheduling

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APPLIED INTELLIGENCE 2022年第15期52卷 18210-18225页

作者： Wang, Jinglian Gong, Bin Liu, Hong Li, Shaohui Ludong Univ Sch Informat & Elect Engn Yantai Peoples R China Shandong Univ Sch Comp Sci & Technol Jinan Peoples R China Shandong Normal Univ Sch Informat Sci & Technol Jinan Peoples R China

This paper focuses on the design and implementation of green heterogeneous scheduling algorithm based on the theory and technology hotspot of energy saving and emission reduction in cloud computing, since intelligent scheduling algorithms often have defects such as insufficient dynamic optimization power or poor parallel framework design. Following that, a distributed swarm intelligence optimization algorithm for greener heterogeneous scheduling, is proposed, including (1) an optimal blending pattern of particle swarm intelligence and nonlinear geometric Brownian motion, based on the related physical sciences and stochastic mathematical analyses, and (2) a parallel fusion model that is suitable for the management server processor hybrid system, i.e., coarse-grained parallelism between nodes and CPU/GPU master-slave in a single node. Then, a large number of experimental results are given, whose evaluation indicators can be divided into two categories: static and dynamic optimization performance. Compared with most newly published scheduling algorithms, there are significant advantages of the proposed algorithm on the dynamic optimization performance for consistent or semiconsistent and large inconsistent scheduling instances, although with the lower improvement for the small inconsistent instances.

关键词： Distributed swarm intelligence particle swarm optimization algorithm Standard Brownian motion Geometric Brownian motion Harmonious blending Heterogeneous green scheduling

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