检索结果-内蒙古大学图书馆

11th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM 2015)

作者： Jun Zhao Zhong Ma Wuhan Digital Engineering Institute China

ISBN: (纸本)9781510821279

Existing virtual network mapping algorithms does not consider resource consumption of intermediate node on communication path usually. Minimum resource consumption or shortest path of physical network is regarded as objective, thereby leading to bottleneck due to insufficient resource of intermediate node on communication path, and affecting performance of the whole physical network and subsequent success rate of virtual network. A virtual network mapping algorithm based on load balancing multi-objective particle swarm optimization is proposed in the paper aiming at the problem. Resource consumption of intermediate node is sufficiently considered in the algorithm, double balance of node load and link load is regarded as objective. Meanwhile, the optimal path of particle swarm optimization algorithm is adopted. Experiments show that the algorithm proposed in the paper can not only realize double balance of node load and link load, but also effectively improve request receiving success rate, overall resource load balance and long-term operation income.

关键词： Load Balancing particle swarm optimization algorithm Virtual Network Mapping Load balancing intermediate node Link Load Telecommunication lines particle swarm optimization Virtual networks particle swarm algorithm Resource utilization physical network

来源：评论

学校读者我要写书评

暂无评论

A particle swarm optimization algorithm based on Orthogonal Design

A Particle Swarm Optimization Algorithm based on Orthogonal ...

引用

2010 IEEE World Congress on Computational Intelligence

作者： Yang, Jie Bouzerdoum, Abdesselam Phung, Son Lam Univ Wollongong Sch Elect Comp & Telecommun Engn Wollongong NSW 2522 Australia

ISBN: (纸本)9781424481262

The last decade has witnessed a great interest in using evolutionary algorithms, such as genetic algorithms, evolutionary strategies and particle swarm optimization (PSO), for multivariate optimization. This paper presents a hybrid algorithm for searching a complex domain space, by combining the PSO and orthogonal design. In the standard PSO, each particle focuses only on the error propagated back from the best particle, without "communicating" with other particles. In our approach, this limitation of the standard PSO is overcome by using a novel crossover operator based on orthogonal design. Furthermore, instead of the "generating-and-updating" model in the standard PSO, the elitism preservation strategy is applied to determine the possible movements of the candidate particles in the subsequent iterations. Experimental results demonstrate that our algorithm has a better performance compared to existing methods, including five PSO algorithms and three evolutionary algorithms.

关键词： algorithm theory elitism preservation strategy evolutionary algorithm evolutionary computation evolutionary strategy generating-and-updating model genetic algorithm multivariate optimization orthogonal design particle swarm optimisation particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Economic Dispatch Considering Ancillary Service Based on Revised particle swarm optimization algorithm

Economic Dispatch Considering Ancillary Service Based on Rev...

引用

6th International Conference on Intelligent Computing

作者： Ma, Xin Liu, Yong North China Univ Water Conservancy & Elect Power Sch Management & Econ Zhengzhou 450011 Henan Province Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9783642149214

A revised strategy particle swarm optimization algorithm is proposed to solve the economic dispatch problems in power systems Many constraints such as ramp rate limits and prohibited zones are taken into account and the loss is also calculated On the basis of strategy particle swarm optimization algorithm a new revised strategy is provided to handle the constraints and make sure the particles to satisfy the constraints The strategy can guarantee the particles to search in or around the feasible solutions area combined with penalty functions The accuracy and speed of the algorithm are improved for the particles will rarely search in the infeasible solutions area and the results also show that the new algorithm has a fast speed high accuracy and good convergence

关键词： Electricity market economic dispatch ancillary service particle swarm optimization algorithm revise strategy

来源：评论

学校读者我要写书评

暂无评论

Utilization Efficiency Forecasting of Moisture Content in Maize Based on particle swarm optimization algorithm and RBF Neural Network

Utilization Efficiency Forecasting of Moisture Content in Ma...

引用

2nd International Conference on Computer and Automation Engineering (ICCAE)

作者： Yu Xiaofang Gao Jvlin Song Guodong Inner Mongol Agr Univ Hohhot 010010 Peoples R China Inner Mongolia Autonomous Reg Seed Control Stn Hohhot 010010 Peoples R China

ISBN: (纸本)9781424455690

Utilization efficiency forecasting of moisture content in maize has a great importance to maize production. RBF neural network is able to universal approximation. PSO-RBF neural network which combines particle swarm optimization (PSO) with RBF neural network is proposed to utilization efficiency forecasting of moisture content in maize. Maize fields of the farms in Henan province are applied to study the utilization efficiency forecasting ability of moisture content in maize by the proposed PSO-RBF neural network method. And BP neural network and normal RBF neural network are applied to compare the PSO-RBF neural network method. By analyzing the experimental results, it is indicated that utilization efficiency forecasting ability of moisture content in maize by PSO-RBF neural network than that by RBF neural network and BP neural network.

关键词： utilization efficiency moisture content RBF neural network particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Integration of particle swarm optimization and Immune Genetic algorithm-Based Dynamic Clustering for Customer Clustering

引用

INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2015年第5期24卷

作者： Kuo, R. J. Lin, S. H. Chen, Zhen-Yao Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 10607 Taiwan Taiwan Semicond Mfg Co Hsinchu 30078 Taiwan De Lin Inst Technol Dept Business Adm New Taipei City 23654 Taiwan

This study intends to present a dynamic clustering (DC) approach based on particle swarm optimization (PSO) and immune genetic (IG) (DCPIG) algorithm, which is able to cluster the data into adequate clusters through data characteristics with pre-specified numbers of clusters. The proposed DCPIG algorithm is compared with three DC algorithms in the literature using Iris, Wine, Glass and Vowel benchmark data sets. The experiment results show that the DCPIG algorithm can achieve higher stability and accuracy than the other algorithms. In addition, the DCPIG algorithm is also applied to a real-world problem considering the customer clustering for a cyber flower shop. Lastly, we recommend different products and services to customers based on the clustering results.

关键词： Cluster analysis dynamic clustering particle swarm optimization algorithm immune genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Grey Model Based particle swarm optimization algorithm for Anticorrosion Reliability Design of Underground Pipelines

引用

2nd International Conference on Advances in Product Development and Reliability (PDR)

作者： Liu, Qinming Dong, Ming Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Coll Econ & Management Shanghai 200052 Peoples R China

ISBN: (纸本)9780878492541

This paper explores the grey model based PSO (particle swarm optimization) algorithm for anti-cauterization reliability design of underground pipelines. First, depending on underground pipelines' corrosion status, failure modes such as leakage and breakage are studied. Then, a grey GM(1,1) model based PSO algorithm is employed to the reliability design of the pipelines. One important advantage of the proposed algorithm is that only fewer data is used for reliability design. Finally, applications are used to illustrate the effectiveness and efficiency of the proposed approach.

关键词： Underground pipeline Reliability design Grey theory particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Fault Diagnosis Analysis with Support Vector Regression and particle swarm optimization algorithm

Fault Diagnosis Analysis with Support Vector Regression and ...

引用

22nd Chinese Control and Decision Conference

作者： Tian, WenJie Liu, JiCheng Beijing Union Univ Beijing Automat Inst Beijing 100101 Peoples R China

ISBN: (纸本)9781424451821

The fault diagnosis model with support vector regression (SVR) and particle swarm optimization algorithm (POSA) for is proposed. The novel structure model has higher accuracy and faster convergence speed. We construct the network structure, and give the algorithm flow. The impact factor of fault behaviors is discussed. With the ability of strong self-learning and faster convergence, this fault detection method can detect various fault behaviors rapidly and effectively. by learning the typical fault characteristic information. Utilizing the character that principal components analysis algorithm can keep the discern ability of original dataset after reduction, the reduces of the original dataset are calculated and used to train individual SVR for ensemble, and consequently, increase the detection accuracy. To validate the effectiveness of the proposed method, simulation experiments are performed based on the electronic circuit dataset. The results show that the proposed method is a promised method owning to its high diversity, high detection accuracy and faster speed in fault diagnosis.

关键词： Support Vector Regression Principal Components Analysis Fault Diagnosis Reduction particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A MODIFIED particle swarm optimization TO IMPROVE PRECISION OF COMPLEX ASSEMBLY

引用

INTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION 2016年第4期31卷 329-340页

作者： Kumar, Ponnaiah Peter Anand Maheswaran, Rathinam Raj, Mathana Singh Victor VV Coll Engn Fac Mech Engn Dept Tisaiyanvilai Tamil Nadu India Mepco Schlenk Engn Coll Fac Mech Engn Dept Sivakasi Tamil Nadu India Sardar Raja Coll Engn Fac Mech Engn Dept Alangulam Tamil Nadu India

An assembly consists of two or more mating parts. The quality of the assembly is mainly based on the quality of mating parts. The mating parts may be manufactured using different machines and processes with different standard deviations. Therefore, the dimensional distributions of the mating parts are not similar. This results in clearance between the mating parts. To obtain high precision assemblies, clearance variation has to be reduced. Selective assembly helps to reduce this clearance variation. Selective assembly is a method of obtaining high precision assembly from relatively low precision components. In selective assembly, the mating parts are manufactured with wide tolerances. The mating part population is then partitioned to form selective groups, and the appropriate selective groups are assembled interchangeably. This paper presents a modified particle swarm optimization (MPSO) to improve the precision of a complex assembly which consists of piston, piston ring and cylinder. The proposed MPSO is based on the concept that an individual in the PSO not only learns from its best experience, but also learns from its mistakes referred as personal worst position. The idea of this modification is based on the social behaviour that each particle tries to leave its previous worst position. The main distinction of this approach is in using particle's worst (but worth) experience instead of the best previous experience. The results show that the proposed approach is more apt to find the global optima of the problem.

关键词： Complex assembly precision particle swarm optimization algorithm selective assembly

来源：评论

学校读者我要写书评

暂无评论

A self-adaptive intelligence gray prediction model with the optimal fractional order accumulating operator and its application

引用

MATHEMATICAL METHODS IN THE APPLIED SCIENCES 2017年第18期40卷 7843-7857页

作者： Zeng, Bo Liu, Sifeng Chongqing Technol & Business Univ Coll Business Planning Chongqing 400067 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing 210016 Jiangsu Peoples R China

The self-adaptive intelligence gray predictive model (SAIGM) has an alterable-flexible model structure, and it can build a dynamic structure to fit different external environments by adjusting the parameter values of SAIGM. However, the order number of the raw SAIGM model is not optimal, which is an integer. For this, a new SAIGM model with the fractional order accumulating operator (SAIGM_FO) was proposed in this paper. Specifically, the final restored expression of SAIGM_FO was deduced in detail, and the parameter estimation method of SAIGM_FO was studied. After that, the particle swarm optimization algorithm was used to optimize the order number of SAIGM_FO, and some steps were provided. Finally, the SAIGM_FO model was applied to simulate China's electricity consumption from 2001 to 2008 and forecast it during 2009 to 2015, and the mean relative simulation and prediction percentage errors of the new model were only 0.860% and 2.661%, in comparison with the ones obtained from the raw SAIGM model, the GM(1, 1) model with the optimal fractional order accumulating operator and the GM(1, 1) model, which were (1.201%, 5.321%), (1.356%, 3.324%), and (2.013%, 23.944%), respectively. The findings showed both the simulation and the prediction performance of the proposed SAIGM_FO model were the best among the 4 models.

关键词： fractional order accumulating operator particle swarm optimization algorithm prediction of China's electricity consumption self-adaptive intelligence gray predictive model

来源：评论

学校读者我要写书评

暂无评论

Iterative response surface joint algorithm analysis of optimization arrangement on mistuned blades

引用

JOURNAL OF VIBROENGINEERING 2017年第6期19卷 4210-4220页

作者： Pan, Honggang Zhao, Tianyu Yuan, Huiqun Li, Bobo Northeastern Univ Sch Mech Engn & Automat Shenyang Liaoning Peoples R China Northeastern Univ Sch Sci Shenyang Liaoning Peoples R China Shenyang Inst Engn Shenyang Liaoning Peoples R China

This paper presents a test method to measure vibration characteristics of mistuned bladed disk system. Modal analysis for the disk is to verify the precision of test bench. Further mistuned parameter identification method is proposed to introduce stiffness detuning. From this the samples can be obtained. On the other hand, the joint optimization analysis system is applied which consists of iterative response surface method and particle swarm optimization algorithm. At the same time, it is proved to have higher accuracy and better generalization ability than standard polynomial response surface method for the problem in this paper. The research results indicate that the amplitudes of optimal solution are smaller than that with random arrangement. The vibration response can be improved obviously, especially for resonance region. So, this paper has a certain universality and application value for blade arrangement based on iterative response surface joint algorithm.

关键词： blade arrangement mistuned parameter identification response measurement particle swarm optimization algorithm iterative response surface

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：