检索结果-内蒙古大学图书馆

18th IEEE World Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Ahmadi, Sajjad Sahnesaraie, Mostafa Andalib Talami, Saeid Hojjati Dini, Fakhteh Zanganeh, Mahnaz Mohebbi Ashgevari, Yazdan Islamic Azad Univ Dept Elect Engn Ardabil Sci & Res Branch Ardebil Iran Alborz Elect Power Distribut Alborz Iran Islamic Azad Univ Dept Elect Engn Mehriz Branch Mehriz Iran Shahid Beheshti Univ Fac Elect & Comp Engn Tehran Iran Islamic Azad Univ Dept Elect Engn Ardabil Branch Ardebil Iran

ISBN: (纸本)9781728131498

With the increasing use of inverters in the industry, the need to reduce the output harmonics is felt more than ever. Interstellar multi-level structures have grown exponentially due to benefits such as harmonic reduction and loss reduction. In multi-level inverters, nonlinear and complex equations become more complex when the number of surfaces increases. One of the ways to find optimal keying angles is to reduce the harmonics of the whole Newton-Raphson method. One of the main disadvantages of the Newton-Raphson method is the strong dependence of the answers to the initial guesses. By choosing different initial guesses, different answers may be obtained or results will not converge. And their solution is not possible by conventional methods of numerical solution of nonlinear equations such as the Newton-Ruffson method. In this thesis, one of a variety of Intelligent algorithms is used to solve the Newton-Raphson method problem. The particle swarm algorithm has been selected based on the proper background in solving problems of high complexity to solve the problem of optimal keying angles in this dissertation. In this thesis, the particle swarm algorithm has been improved. Simulation results for various multilevel converters have been presented and represent the high efficiency of the improved type of particle swarm algorithm in determining the appropriate angle of fire for reducing the high harmonics and generating a waveform with very low harmonic distortion and near sinusoidal.

关键词： Multilevel Inverters Selective Harmonic Elimination particle swarm optimization algorithm Improved particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Low-delay layout planning based on improved particle swarm optimization algorithm in 5G optical fronthaul network

引用

OPTICAL FIBER TECHNOLOGY 2021年 67卷

作者： Wang, Nan Liu, Jianfei Lu, Jia Zeng, Xiangye Zhao, Xinyu Hebei Univ Technol Sch Elect & Informat Engn Tianjin 300401 Peoples R China Tianjin Key Lab Elect Mat & Devices Tianjin 300401 Peoples R China Hebei Key Lab Adv Laser Technol & Equipment Tianjin 300401 Peoples R China

The rapid development of time-sensitive applications, such as autopilot, telemedicine and industrial automation, demands more stringent requirements on end-to-end delay. Therefore, low-delay planning of 5G networks is supposed to be a crucial stage. In this paper, a low-delay layout planning based on the nonlinear decreasing inertia weight particle swarm optimization algorithm (NL-wPSO) is demonstrated. By iteratively calculating the velocity and position of each particle, the proposed algorithm can optimize the layout of AAUs in the fronthaul network. Thereby, the distance between the AAUs and edge computing server will be shortened. Compared to the basic PSO, the simulation results indicate a significant reduction on transmission delay.

关键词： 5G Layout planning Edge computing service particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Improved subgroup method coupled with particle swarm optimization algorithm for intra-pellet non-uniform temperature distribution problem

引用

ANNALS OF NUCLEAR ENERGY 2021年 153卷 108070-108070页

作者： Li, Song Zhang, Qian Zhang, Zhijian Zhao, Qiang Liang, Liang Harbin Engn Univ Fundamental Sci Nucl Safety & Simulat Technol Lab Coll Nucl Sci & Technol 145 Nantong St Harbin 150001 Heilongjiang Peoples R China

Due to the Doppler broadening effect, the conventional subgroup weights of different temperatures correspond to different energy ranges. Therefore, the conventional subgroup fixed source equation could not be established without approximation between different temperatures. In this study, the improved subgroup method coupled with the particle swarm optimization algorithm (ISPSO) is proposed. The subgroup fixed source equation is solved for different temperatures by sharing the same set of subgroup weights, which are calculated by generationally updating the particle position during the particle swarm optimization. Afterward, the partial subgroup levels are obtained by the least square method. Besides, ISPSO adopts the fine mesh structure for the resonance interference treatment, then the multigroup effective resonance cross section is obtained by the group condensation. The subgroup parameters of ISPSO could be either generated online or pre-determined. The numerical verification indicates that ISPSO could accurately handle the resonance treatment for the complicated temperature conditions. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Resonance self-shielding Subgroup method Subgroup parameters Non-uniform temperature distribution particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A new bifuzzy optimization method for remanufacturing scheduling using extended discrete particle swarm optimization algorithm

引用

COMPUTERS & INDUSTRIAL ENGINEERING 2021年 156卷 107219-107219页

作者： Shi, Jiaxuan Zhang, Wenyu Zhang, Shuai Chen, Jie Zhejiang Univ Finance & Econ Sch Informat Management & Artificial Intelligence Hangzhou 310018 Peoples R China

As the energy shortage and environment pollution become increasingly deteriorating, remanufacturing has become a hot research field for its energy-saving and environmental advantages. In remanufacturing systems, scheduling is a key step that directly affects the successful realization of remanufacturing benefits. Since the remanufacturing involves many two-fold uncertainties, scheduling for remanufacturing is more challenging than for traditional manufacturing. However, few studies have addressed these two-fold uncertainties in remanufacturing environments. Thus, this study proposes a new bifuzzy remanufacturing scheduling model, which handles many two-fold uncertainties in remanufacturing and applies the bifuzzy variable to describe these uncertainties. Multiple non-identical parallel processing lines for remanufacturing end-of-life products have also been integrated into the proposed model. An extended discrete particle swarm optimization algorithm with an effective representation scheme is proposed to solve the model. In the presented algorithm, three new guiding directions and a new multi-directional guiding strategy are designed to enhance the diversity of population, and a new position updating mechanism, local search strategy, and anti-stagnation mechanism are integrated to improve the algorithmic performance. Experiments are performed to verify the effectiveness of the proposed model by comparing it with the traditional deterministic model, and the effectiveness of the proposed algorithm in solving this model by comparing it with other baseline algorithms.

关键词： Remanufacturing scheduling Bifuzzy theory Bifuzzy variable Multiple non-identical parallel processing lines particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Customer segmentation using K-means clustering and the adaptive particle swarm optimization algorithm

引用

APPLIED SOFT COMPUTING 2021年第PartB期113卷 107924-107924页

作者： Li, Yue Chu, Xiaoquan Tian, Dong Feng, Jianying Mu, Weisong China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China Minist Agr Key Lab Viticulture & Enol Beijing 100083 Peoples R China

The improvement of enterprise competitiveness depends on the ability to match segmented customers in a competitive market. In this study, we propose a customer segmentation method based on the improved K-means algorithm and the adaptive particle swarm optimization (PSO) algorithm. The current PSO algorithm can easily fall into a local extremum;thus, adaptive learning PSO (ALPSO) is proposed to improve the optimization accuracy. On the basis of the analysis of population-based optimization, the inertia weight, learning factors, and the position update method are redesigned. To prevent the K-means clustering algorithm from depending on initial cluster centres, the ALPSO algorithm is used to optimize the K-means cluster centres (KM-ALPSO). Aimed at the issue of clustering the actual grape-customer consumption mixed dataset, factor analysis is used to extract numerical variables. We then propose a dissimilarity measurement method to cluster the mixed data. We compare ALPSO with several parameter update methods. We also conduct comparative experiments to compare KM-ALPSO on five UCI datasets. Finally, the improved KM-ALPSO (IKM-ALPSO) clustering algorithm is applied in customer segmentation. All results show that the three proposed methods outperform existing models. The experimental results also demonstrate the effectiveness and practicability of IKM-ALPSO for customer segmentation. (C) 2021 Elsevier B.V. All rights reserved.

关键词： K-means clustering algorithm particle swarm optimization algorithm Adaptive parameter learning Mixed data Customer segmentation

来源：评论

学校读者我要写书评

暂无评论

Operation scheduling of a coal-fired CHP station integrated with power-to-heat devices with detail CHP unit models by particle swarm optimization algorithm

引用

ENERGY 2021年 214卷 119022-119022页

作者： Liu, Ming Wang, Shan Yan, Junjie Xi An Jiao Tong Univ State Key Lab Multiphase Flow Power Engn Xian 710049 Peoples R China

The accommodation of high-penetration renewable power poses a considerable challenge to power grids. Coal-fired combined heat and power (CHP) stations are forced to enhance their operational flexibility by applying heat-power decoupling technologies. Power-to-heat devices, including electric boilers and heat pumps, are capable to enhance the operational flexibility of coal-fired CHP stations. The problem regarding the operation scheduling of a CHP station with multiple CHP units and power-to-heat devices is addressed in this study. Operation optimization models integrated with detail CHP unit models are developed, and the particle swarm optimization algorithm is utilized as the optimization algorithm. Then, a case study are carried out. Results show that the unequal distribution of heating and power loads among coal-fired CHP units can decrease the total irreversibility caused by heating steam pressure regulation. The operation scheduling method provided in this study can decrease the total coal consumption by 14.14 and 14.70 t/day for the CHP station integrated with an electric boiler and a heat pump, respectively. As a result, 1204.7 and 1252.44 ton CO2, and an additional similar to 182 and-190 kUSD/year can be saved for the reference CHP station integrated with an electric boiler and a heat pump, respectively. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： CHP Energy saving Flexibility Operation scheduling particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A Quantum particle swarm optimization algorithm with Available Transfer Capability 19

A Quantum Particle Swarm Optimization Algorithm with Availab...

引用

19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)

作者： Qu Liping Meng Yan Li Dongheng Xue Hai-bo Beihua Univ Engn Training Ctr Jilin 132011 Jilin Peoples R China Jilin Prov Ind Technician Coll Dept Elect Engn Jilin 132011 Jilin Peoples R China State Grid Jilin Elect Power Co Ltd Shulan Power Supply Co Jilin 132011 Jilin Peoples R China Jilin Municipal Govt Jilin 132011 Jilin Peoples R China

ISBN: (数字)9781728197241

ISBN: (纸本)9781728197241

Quantum particle swarm algorithm integrated the quantum behavior with particle swarm optimization algorithm, is used to settle the majorization question of calculating available transmission capability. And by using the software of Matlab to IEEE-30 bus system as an example of the simulation, after comparing the simulation results with the traditional particle swarm optimization algorithm results, we dissected the optimization performance and convergence speed of the above two algorithms, and verify the effectiveness of quantum particle swarm algorithm to settle the majorization question of the available transmission capability.

关键词： available transmission capacity particle swarm optimization algorithm quantum particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A Novel Competitive particle swarm optimization algorithm Based on Levy Flight

A Novel Competitive Particle Swarm Optimization Algorithm Ba...

引用

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

来源：评论

学校读者我要写书评

暂无评论

Indoor positioning system based on particle swarm optimization algorithm

引用

MEASUREMENT 2019年 134卷 908-913页

作者： Guo, Hang Li, Huixia Xiong, Jian Yu, Min Nanchang Univ Sch Resources Environm & Chem Engn Key Lab Poyang Lake Environm & Resource Utilizat Minist Educ Nanchang 330031 Jiangxi Peoples R China Nanchang Univ Nanchang 330031 Jiangxi Peoples R China Univ New Brunswick Dept Geodesy & Geomat Engn Fredericton NB Canada Jiangxi Normal Univ Nanchang 330022 Jiangxi Peoples R China

In recent years, wireless sensor networks localization becomes a crucial method in the indoor positioning. Following the frontiers of technology, we studied on ZigBee wireless sensor network. Since the parameters of the path loss model are difficult to be estimated by the ordinary methods, the particle swarm optimization (PSO) method is proposed in this paper to simulate the parameter estimation in the indoor environment. The Texas Instruments CC2530 chip was also taken to build a ZigBee wireless sensor network. The data collected from ZigBee wireless sensor network experiment could be used to estimate the model parameters. PSO algorithm for fitting the signal attenuation curve removed the poor experimental data, and the output model fit well with the signal attenuation curve. Experimental results demonstrate that the PSO algorithm works well, clear, easy to understand, and has a high reliability. Using the parametric model to locate the user's position, and with the weighted K-Nearest Neighbor algorithm, the two-dimensional (2D) positioning was improved effectively. The standard deviation of 2D positioning is 1.15 m, so the model has practical value. Through the experiment and analyzing the data, it is verified that the proposed PSO algorithm in this paper is better than the previous methods we presented. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： ZigBee Path loss model particle swarm optimization algorithm Parameter estimation K-Nearest Neighbor algorithm

来源：评论

学校读者我要写书评

暂无评论

A hybrid particle swarm optimization algorithm for load balancing of MDS on heterogeneous computing systems

引用

NEUROCOMPUTING 2019年 330卷 380-393页

作者： Li, Dapu Li, Kenli Liang, Jie Ouyang, Aijia Hunan Univ Coll Informat Sci & Engn Changsha 410082 Hunan Peoples R China Zunyi Normal Univ Sch Informat Engn Zunyi 563006 Guizhou Peoples R China

An efficient hybrid genetic algorithm and particle swarm optimization algorithm (HGAPSO) is studied in this work for load balancing of molecular dynamics simulations (MDS) on heterogeneous supercomputers by combining the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm. A hybrid CPU-GPU platform is applied to enabling large-scale MDS that emulates the metal solidification. Applied to task scheduling of the parallel algorithm, the approach obtains excellent results. The experimental results show that the proposed algorithm can improve the efficiency of parallel computing and the precision of physical simulation. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Genetic algorithm Load balancing Metal solidification Molecular dynamics simulation particle swarm optimization algorithm Supercomputer

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

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

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

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

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：