检索结果-内蒙古大学图书馆

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

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Multi-agent learning mechanism design and simulation of multi-echelon supply chain

引用

COMPUTERS & INDUSTRIAL ENGINEERING 2022年 168卷

作者： Sun, Jun-Yan Tang, Jian-Ming Chen, Zhi-Rui Shaanxi Univ Sci & Technol Fac Mech & Elect Engn Xian 710021 Peoples R China Xian Aerosp Precis Electromech Inst Xian 710010 Peoples R China

A supply chain multi-agent learning mechanism based on the particle swarm optimization algorithm is designed. The manufacturer's profit and the product utility are taken as objective functions to explore the influences of social learning ability and self-learning ability on the manufacturer's optimal price, optimal advertising in-vestment, optimal profit, product utility, and supply chain competition. This research demonstrates the in-fluences of different learning abilities on the evolution of the supply chain. The simulation results show that the most appropriate social learning ability can enhance competition between manufacturers and improve product utility. With the enhancement of self-learning ability, manufacturers have a wider range of pricing, products are more diverse, and consumers have a wider choice of goods. This study shows certain guiding significance for the scientific management of the supply chain and the optimization of the competitive strategy of the enterprises on the chain.

关键词： particle swarm optimization algorithm Multi-echelon supply chain Multi-agent Learning mechanism Simulation analysis

来源：评论

学校读者我要写书评

暂无评论

optimization algorithm for E-commerce Order Delivery Route 2

Optimization Algorithm for E-commerce Order Delivery Route

引用

2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Dong, Dandan Shaanxi Fashion Engineering University School of Economics and Management Xi'an China

ISBN: (纸本)9798350317558

With the rapid development of e-commerce, logistics distribution has become an important means for e-commerce enterprises to improve their competitiveness. This article focuses on the optimization problem of e-commerce order delivery routes, using particle swarm optimization algorithm for research, aiming to improve delivery efficiency, reduce costs, and enhance customer satisfaction. This article compares the time for solving the optimal path before and after optimization. The experimental results show that after optimizing with the particle swarm optimization algorithm, the average solving time is reduced from 67.12s to 27.49s. The research results indicate that the particle swarm optimization algorithm is superior to traditional algorithms in terms of solving efficiency and quality, providing strong support for e-commerce enterprises in fierce market competition. © 2024 IEEE.

关键词： E-commerce Order Experimental Testing Order Delivery particle swarm optimization algorithm Route optimization

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Dynamic performance analysis and optimization of pulp-molding machine frames

引用

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2022年第1期122卷 209-217页

作者： Lou, Junbin Gong, Yaping Zhejiang Ocean Univ Sch Marine Engn Equipment Zhoushan 316000 Peoples R China Jiaxing Univ Coll Informat Sci & Engn Jiaxing 314001 Zhejiang Peoples R China Zhejiang Ocean Univ Maritime Sch Zhoushan 316000 Peoples R China

The pulp-molding product is a new degradable and pollution-free packaging material replacing traditional packaging materials. Countries are strongly recommending that pulp-molding machines are mainly used to produce pulp-molding products. The stability, efficiency, and rapidity of its products' molding and processing quality are largely controlled by the structural dynamic mechanics of the molding machine frame. Its equipment's dynamic performance is measured by the 1st-order natural frequency. The pulp-molding machine frame was taken as an example for optimizing the design and investigating the dynamic performance in the work. Based on finite elements, the beam structure was divided into finite elements, and the global independent generalized displacement coordinates of equipment were extracted using multi-point constraint elements. The global independent generalized displacement coordinates and the Lagrange equation were used to establish the device's elastic dynamic model. Besides, the correctness of the theoretical model was verified by the finite element method. Finally, the first natural frequency was taken as the optimization design goal, and the particle swarm optimization algorithm was utilized to optimize the section size of the beam in equipment. The results could optimize the molding machine's design and analyze the dynamic performance at the pre-design stage.

关键词： Pulp-molding machine Elastodynamic modeling Natural frequency particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

optimization of Multi-Objective Unequal Area Facility Layout

引用

IEEE ACCESS 2022年 10卷 38870-38884页

作者： Tang, Hongtao Ren, Senli Jiang, Weiguang Chen, Qingfeng Zhejiang Univ Technol Coll Mech Engn Hangzhou 310023 Peoples R China

The unequal area facility layout problem (UA-FLP) refers to the reasonable arrangement of a certain number of facilities in a given layout area. The facility layout should satisfy given layout constraints and optimize given optimization objectives as far as possible. In this paper, a method combining improved lowest horizontal line method and particle swarm optimization algorithm is proposed to solve UA-FLP, which achieves multi-objective optimization of material handling cost, the adjacent value and the utilization rate of floor shop. On the one hand, the algorithm formulates facility packing rules through the improved lowest horizontal line method, which simplifies the legalization of facility layout. On the other hand, a modified particle swarm optimization (PSO) algorithm combining objective space division method (OSD) and niche technology is used for multi-objective optimization. The proposed algorithm overcomes the shortcomings of previous facility layout methods such as complex overlapping interference process, large amount of calculation and long time for multi-objective optimization. Compared with the comparison methods, the results show that material handling cost is reduced by 1%-6% and the utilization ratio of floor shop is increased by 2%-7%.

关键词： Layout optimization particle swarm optimization Conferences Costs Materials handling Interference Facility layout lowest horizontal line particle swarm optimization algorithm niche technology

来源：评论

学校读者我要写书评

暂无评论

Multiscale Joint optimization Strategy for Retinal Vascular Segmentation

引用

SENSORS 2022年第3期22卷 1258-1258页

作者： Yan, Minghan Zhou, Jian Luo, Cong Xu, Tingfa Xing, Xiaoxue Changchun Univ Coll Elect Informat Engn Changchun 130012 Peoples R China Beijing Inst Technol Sch Opt & Photon Beijing 100081 Peoples R China

The accurate segmentation of retinal vascular is of great significance for the diagnosis of diseases such as diabetes, hypertension, microaneurysms and arteriosclerosis. In order to segment more deep and small blood vessels and provide more information to doctors, a multi-scale joint optimization strategy for retinal vascular segmentation is presented in this paper. Firstly, the Multi-Scale Retinex (MSR) algorithm is used to improve the uneven illumination of fundus images. Then, the multi-scale Gaussian matched filtering method is used to enhance the contrast of the retinal images. Optimized by the particle swarm optimization (PSO) algorithm, Otsu algorithm (OTSU) multi-threshold segmentation is utilized to segment the retinal image extracted by the multi-scale matched filtering method. Finally, the image is post-processed, including binarization, morphological operation and edge-contour removal. The test experiments are implemented on the DRIVE and STARE datasets to evaluate the effectiveness and practicability of the proposed method. Compared with other existing methods, it can be concluded that the proposed method can segment more small blood vessels while ensuring the integrity of vascular structure and has a higher performance. The proposed method has more obvious targets, a higher contrast, more plentiful detailed information, and local features. The qualitative and quantitative analysis results show that the presented method is superior to the other advanced methods.

关键词： medical image retinal vascular matching filter multiscale particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

optimization procedures for the inversion of impedance spectra to the distribution of relaxation times

引用

JOURNAL OF ELECTROANALYTICAL CHEMISTRY 2022年 911卷 116199-116199页

作者： Wang, Chunlin Zhu, Guangyan Zhang, Pengbo Fang, Xiaohong Chinese Acad Sci Shanghai Adv Res Inst Shanghai 201210 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Pylon Technol Co Ltd Shanghai 201210 Peoples R China

Tikhonov regularization technology has been applied to solve the ill-posed inversion problem of electrochemical impedance spectra inversion to the distribution of relaxation times (DRT). However, the parameters contributing to the accuracy of the DRT obtained by this method, including types of the EIS data, regularization matrixes, regularization parameters, and shape factors, need sophisticated adjustment. In this work, the influences of these parameters were discussed in detail, and the results showed that the effects of some process parameters on the DRT worked complementarily. Therefore, the DRT inversion program with the particle swarm optimization algorithm (PSO), a multi-objective global optimization algorithm, was developed. It was demonstrated the accuracy, feasibility, and reliability of the proposed DRT program. The program was also applied to separate and quantify the electrochemical processes of the commercial LIBs.

关键词： Electrochemical impedance spectroscopy particle swarm optimization algorithm Distribution of relaxation time Tikhonov regularization

来源：评论

学校读者我要写书评

暂无评论

Degradation prediction model of PEMFC based on multi-reservoir echo state network with mini reservoir

引用

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2022年第94期47卷 40026-40040页

作者： Zhang, Shaojie Chen, Tao Xiao, Fei Zhang, Rufeng Wuhan Univ Technol Sch Mech & Elect Engn 122 Luoshi Rd Wuhan 430070 Peoples R China

The durability of the proton exchange membrane fuel cell (PEMFC) has always been a major obstacle in its commercialization process and effective degradation prediction can improve this problem to a certain extent. Data-driven degradation prediction model is one of the most effective prediction methods available, which is able to ignore the structure of the PEMFC itself and rely solely on the data to make predictions, greatly simplifying the pre-diction process. Echo state network (ESN), as one of the data-driven methods, has received much attention for its low computational complexity and fast convergence in the degra-dation prediction of PEMFC. In this paper, the multi-reservoir echo state network with mini reservoir (MRM) degradation prediction model of PEMFC is proposed. The structure of MRM is that the main reservoirs are stacked in a layer and the mini reservoir is in the next level to collect and organize the main reservoir states. In addition, in order to improve the prediction accuracy, this paper firstly uses Savitzky-Golay (SG) filter to process the original data, and then investigates the influence of two important parameters, the number of main reservoirs and the number of main reservoir neurons, on the prediction accuracy and finds the optimal number of main reservoirs and main reservoir neurons for this model using particle swarm optimization (PSO) algorithm. Finally, the effectiveness of the model is verified on different lengths of training sets under both static and dynamic conditions. The results show that the model has higher accuracy and better robustness in the PEMFC degradation prediction compared with other models. (c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Degradation prediction Main reservoir Savitzky-golay filter Multi-reservoir echo state network with mini reservoir particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：