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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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Structure optimization based on phase-locked loop and controller parameters optimization of Y-connected modular multi-level converter for fractional frequency offshore wind power system under weak grid

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IET GENERATION TRANSMISSION & DISTRIBUTION 2022年第18期16卷 3605-3616页

作者： Meng, Yongqing Jia, Feng Wu, Kang Duan, Ziyue Wang, Xiuli Yang, Yong Wang, Xifan Xi An Jiao Tong Univ State Key Lab Elect Insulat & Power Equipment Xian Shaanxi Peoples R China Xi An Jiao Tong Univ Shaanxi Key Lab Smart Grid Xian Shaanxi Peoples R China Zhejiang Windey Co Ltd Hangzhou 310000 Zhejiang Peoples R China State Grid Gansu Elect Power Co Elect Power Res Inst Lanzhou Gansu Peoples R China

Based on a practical long-distance large-capacity transmission engineering case, the small-signal stability of fractional frequency transmission system (FFTS) with Y-connected modular multi-level converter (Y-MMC) is mainly researched in this paper. Different from the previous control strategies of Y-MMC, this paper establishes a frequency decoupling mathematical model and proposes the decoupling control strategies. Then, the small-signal model of the Y-MMC system is obtained. Considering the existence of phase locked loop (PLL) will deteriorate the stability of the system under weak grid, this paper applies the short-circuit ratio (SCR) for analysis and proposed a new structure of PLL, called phase-shifted PLL (PS-PLL), to improve the small-signal stability of the system. In addition, for better stability and dynamic response performance of the closed-loop control system, this paper proposes a new parameter optimization objective function for parameters adjustment. Controller parameters optimization is conducted by using the particle swarm optimization (PSO) algorithm and stability evaluation of the system is conducted based on the eigenvalue distribution. Finally, the feasibility and superiority of the proposed strategies and optimization are verified in MATLAB/ Simulink.

关键词： Y-MMC system PS-PLL fractional frequency transmission system Matlab-Simulink power system stability wind power plants particle swarm optimisation phase locked loop power transmission control power grids Y-MMC closed loop systems frequency decoupling mathematical model eigenvalue distribution HVDC power convertors system small-signal stability particle swarm optimization algorithm closed-loop control system SCR small-signal model offshore installations parameter optimization objective function voltage-source convertors eigenvalues and eigenfunctions fractional frequency offshore wind power system phase locked loops FFTS phase-locked loop weak grid controller parameter optimization HVDC power transmission parameter adjustment dynamic response decoupling control strategies structure optimization PSO algorithm power generation control phase-shifted PLL Y-connected modular multilevel converter large-capacity transmission engineering

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An efficient hybrid approach based on PSO, ABC and k-means for cluster analysis

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第14期81卷 19321-19339页

作者： Pu, Qiumei Gan, Jingkai Qiu, Lirong Duan, Jiaxin Wang, Hui Minzu Univ China Sch Informat Engn Beijing 100081 Peoples R China

particle swarm optimization (PSO) algorithm is one of the typical example of swarm Intelligence (SI) algorithm. This article addresses such problems of PSO algorithm as random initial position of each particle, unsmooth speed weight change, and poor search ability, and proposes an optimization algorithm-hybrid PSO (HPSO) algorithm to solve these problems. This algorithm makes comprehensive improvements to the PSO clustering algorithm by using the K-means clustering algorithm to generate initial clustering centers, adopting a negative exponential function model to update the weight of velocity when constructing the "position-velocity" model, and introducing the "search restriction" mechanism, and the "fly-back" mechanism and auxiliary search methods such as the single point crossover operator in the Artificial Bee Colony (ABC) algorithm. Furthermore, experimental results were analyzed and verified. The experiment compares HPSO algorithm with K-Means algorithm, PSO algorithm, and other two typical improved algorithms from the literature on six of the UCI standard clustering test data sets. The results indicate that HPSO algorithm has good performance in stability, clustering effectiveness, robustness and global search ability.

关键词： swarm intelligence algorithm particle swarm optimization algorithm HPSO algorithm K-means algorithm Single point crossover operator

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New control strategy for suppressing the local vibration of sandwich beams based on the wave propagation method

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JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES 2022年第1期33卷 231-247页

作者： Xu, Jun Chen, Yandong Tai, Yongpeng Shi, Guodong Chen, Ning Yao, Jialing Nanjing Forestry Univ Dept Mech & Elect Engn 159 Longpan Rd Nanjing 210037 Peoples R China Nanjing Forestry Univ Dept Automobile & Traff Engn Nanjing Peoples R China

The general vibration control strategy of beam structures is a global vibration control method based on the principle of modal superposition. However, the vibration wave control of beams can achieve local control of the vibration energy. This paper presents a wave control method for the local vibration control of fractional viscoelastic composite beams based on the operating principle of a piezoelectric sheet. To obtain better control performance, a particle swarm optimization algorithm was adopted to optimize the parameters of the piezoelectric sheet. A linear quadratic regulator control algorithm was designed to verify the validity of the proposed method. In addition, the effects of the piezoelectric sheet number and fractional order on the amplitude response and optimization parameters were investigated. We observed that the proposed method has a good control effect on the local area vibration, and it can control the flow direction of the vibration power. The proposed method can be used to directly design the voltage and phase of a piezoelectric sheet without real-time feedback computation. This method is suitable for reducing local vibration under single repetitive operating conditions in engineering and can provide a theoretical basis for a follow-up study on the acoustic black hole phenomenon.

关键词： Fractional order composite beam piezoelectric sheet local vibration control particle swarm optimization algorithm wave control method

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Damage localization in pressure vessel using guided wave-based techniques: Optimizing the sensor array configuration to mitigate nozzle effects

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APPLIED ACOUSTICS 2022年 185卷 108393-108393页

作者： Hu, Chaojie Yang, Bin Xiao, Biao Xuan, Fu-Zhen Xiang, Yanxun East China Univ Sci & Technol Sch Mech & Power Engn Shanghai Peoples R China Tongji Univ Sch Aerosp Engn & Appl Mech Shanghai Peoples R China Shanghai Inst Special Equipment Inspect & Tech Re Shanghai Peoples R China

Ultrasonic guided waves are desirable technologies for damage monitoring in large structures such as pipes and pressure vessels since they could propagate over a long distance with low attenuation. However, due to the complex scattering of guided waves, it is a great challenge to extract the damage information when using guided wave-based techniques to monitor the pressure vessels equipped with accessories. As an attempt, we investigated the interactive behavior between guided waves and pressure vessel nozzles. Ensemble empirical mode decomposition, fast Fourier transform and ellipse-based damage localization algorithm are adopted to perform the signal processing and damage localization analysis, respectively. A sensor array configuration optimization method is presented to mitigate the nozzle effects on the propagation of guided waves. The optimal number of sensors is determined experimentally, and the sensor array placement is optimized by a particle swarm optimization algorithm. The accurate experimental damage localization results in the pressure vessel and curved plate indicate the effectiveness of our methods. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Pressure vessel nozzle Sensor array Guided wave Damage localization particle swarm optimization algorithm

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Hybrid PSO-GA algorithm for Estimation of Magnetic Anomaly Parameters Due to Simple Geometric Structures

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PURE AND APPLIED GEOPHYSICS 2022年第6-7期179卷 2231-2254页

作者： Sohouli, A. N. Molhem, H. Zare-Dehnavi, N. Islamic Azad Univ Petr Min & Mat Engn Dept Cent Tehran Branch Tehran Iran Islamic Azad Univ Dept Phys Karaj Branch Karaj Iran Islamic Azad Univ Dept Phys Cent Tehran Branch Tehran Iran

The optimization algorithm is a very fast method for modeling magnetic anomalies from an ideal geological model. In addition, these models can be used to explore and estimate mineral deposits. However, in exploratory geophysics, it is common to use modeling methods with regular geometric shapes to estimate anomalous parameters such as shape, material, depth, and elongation angles. In this study, the parameters of the magnetic model are estimated using a hybrid PSO-GA method, which is an evolutionary algorithm based on a combination of particle swarm optimization (PSO) and genetic algorithms (GA). In this method, the PSO algorithm improves the magnetic data, while the GA modifies the decision to estimate the model parameters. Moreover, the balance between exploration and exploitation capabilities improves the performance of this method by combining genetic operators in the hybrid PSO-GA algorithm. Here, Gaussian white noise has been added to the artificial data with different percentages in the range of 0-25% to analyze the error of the obtained model. The results show that the proposed method can provide valuable outcomes in estimating model parameters up to 25% noise. Moreover, the accurate data from an aeromagnetic survey in the Basiran region in South Khorasan province are used to validate the estimated model parameters. The results show that the estimation of the parameters obtained from the current method is consistent with the parameters given from the commercial software and the published geological results.

关键词： Parameter estimation parameters of the magnetic model magnetic anomalies modeling particle swarm optimization algorithm genetic algorithm airborne magnetic data

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Classification algorithm for class imbalanced data based on optimized Mahalanobis-Taguchi system

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APPLIED INTELLIGENCE 2022年第9期52卷 10674-10691页

作者： Mao, Ting Zhou, Li Zhang, Yueyi Sun, Yefang China Jiliang Univ Sch Econ & Management Hangzhou 310000 Peoples R China China Natl Inst Standardizat Beijing 100000 Peoples R China

Imbalanced data classification is a challenge in data mining and machine learning. To improve the classification performance for imbalanced data, this paper proposes an imbalanced data classification algorithm based on the optimized Mahalanobis-Taguchi system (OMTS). At the feature selection stage, important feature variables are determined by four principles, namely maximizing mutual information between features and classes, minimizing mutual information between features, maximizing the initial classification accuracy, and selecting features that produce not only the local maximum or minimum of the difference between the mean Mahalanobis distances (MDs) of normal and abnormal samples but also the largest number of features. At the threshold determination stage, using the selected features, particle swarm optimization is used to determine the optimal threshold for classifying normal and abnormal samples according to the principle of maximizing classification accuracy. At the classification and discrimination stage, the samples are divided into two classes according to their MDs and optimal threshold. Experimental results show that OMTS obtains 0.92, 0.95, 0.81, 0.88, and 0.74 in accuracy on the Forest Type Mapping UCI, Fetal Health Classification, Connectionist Bench, Wine Quality, and Oil datasets, respectively, and has better classification performance than other algorithms.

关键词： Imbalanced data Classification Mahalanobis-Taguchi system Mutual information particle swarm optimization algorithm

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