检索结果-内蒙古大学图书馆

Based on particle swarm optimization algorithm of Cloud Computing Resource Scheduling in Mobile Internet

INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING 2016年第6期9卷 25-34页

作者： Lin, Yong Qingdao Vocat & Tech Coll Hotel Management Qingdao Shandong Peoples R China

Mobile Internet due to the limitation of the mobile terminal power supply, transmission and calculation of the need to adopt energy saving strategy, also due to the terminal mobility, mobile Internet topology change. Therefore, mobile Internet cloud computing resources allocation need both energy efficiency and assure the time characteristics of mobile business;Aiming at this problem, this paper presents a maximum energy efficiency optimization, in the restrictive conditions at the same time, guarantee the minimum time delay the business. According to the characteristics of the optimization problems, both the distribution of the improved algorithm is proposed, the algorithm based on particle swarm optimization (pso) algorithm, build the search direction matrix of orientation, the simulation results show that the proposed allocation algorithm can effectively improve the efficiency of energy utilization, and ensure that the time delay of the business requirements.

关键词： The Mobile Internet Cloud Computing particle swarm optimization algorithm Resource Scheduling

来源：评论

学校读者我要写书评

暂无评论

Vehicle Fuel Consumption Prediction Based on Least Squares Support Vector Machine Optimized by Improved particle swarm optimization algorithm

Vehicle Fuel Consumption Prediction Based on Least Squares S...

引用

第19届亚太汽车工程年会暨2017中国汽车工程学会年会

作者： Liu Yu Li Mengliang Xu Yueyun China Automotive Technology&Research Center

Firstly,this paper summarizes the characteristics of vehicle running characteristics and design parameters,which have influence on vehicle fuel ***,200 vehicles test results are used as training samples,with sensitive features and fuel consumption of type approve test as the input parameters,and the actual vehicle fuel consumption as output *** vehicle fuel consumption prediction model based on Least squares support vector machine(LSSVM) optimized by the improved particle swarm optimizationalgorithm(IPSO) is ***,the vehicle fuel consumption prediction model is used to predict the fuel consumption of another 100 *** results show that the prediction error of test samples are less than 5%,and the fuel consumption prediction model proposed in this paper has fully considered the impact of vehicle operating characteristics and design parameters on fuel *** addition,the fuel consumption predictionmodel has high prediction accuracy and reliability than some traditional methods such as back propagation neural network(BPNN).

关键词： least squares support vector machine particle swarm optimization algorithm vehicle fuel consumption prediction

来源：评论

学校读者我要写书评

暂无评论

Traffic Prediction Model Based on particle swarm optimization algorithm

Traffic Prediction Model Based on Particle Swarm Optimizatio...

引用

2017 4th PMSS International Conference on Environmental Studies, Health Services and Social Sciences (EHS 2017)

作者： Qingyang Yao Jiangxi Modern Polytechnic College

Computer network traffic prediction plays an important role in the control and adjustment of network traffic,and then improves the network performance and service *** prediction accuracy of the traditional computer network flow method is low,only about 85%.Aiming at the nonlinear and time-varying characteristics of network traffic,it is difficult to accurately realize network traffic *** order to solve this problem,we propose a network traffic prediction method based on chaotic particle swarm optimization *** vector regression(SVR) is a support vector machine model for trend prediction,which can find the global optimal ***,the choice of SVR parameters plays a decisive role in the optimization of regression *** chaotic particle swarm optimization(CPSO) algorithm is used to optimize the support vector parameters,and the network traffic prediction model is established by establishing the chaotic particle swarm optimization *** simulation results show that the chaotic particle swarm optimization SVR network traffic prediction model has strong ability and good effect.

关键词： particle swarm optimization algorithm Traffic prediction model Data analysis

来源：评论

学校读者我要写书评

暂无评论

Hybrid particle swarm optimization-Firefly algorithm (HPSOFF) for combinatorial optimization of non-slicing VLSI floorplanning

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2017年第1期32卷 661-669页

作者： Sivaranjani, P. Kumar, A. Senthil Kongu Engn Coll Dept Elect & Commun Engn Erode Tamil Nadu India Dr Mahalingam Coll Engn & Technol Dept Elect & Elect Engn Pollachi Tamil Nadu India

Floorplanning is the initial step in the process of designing layout of the chip. It is employed to plan the positions and shapes of modules during the process of VLSI Design cycle to optimize the cost metrics like layout area and wirelength. In this paper, a Hybrid particle swarm optimization-Firefly (HPSOFF) algorithm is proposed which integrates particle swarm optimization (PSO), Firefly (FF) and Modified Corner List (MCL) algorithms. Initially, PSO algorithm utilizes MCL algorithm for non-slicing floorplan representations and fitness value evaluation. The solutions obtained from PSO are provided as initial solutions to FF algorithm. Fitness function evaluation and floorplan representations for FF algorithm are again carried out using MCL algorithm. The proposed algorithm is illustrated using Microelectronics Centre of North Carolina (MCNC) and Gigascale Systems Research Centre (GSRC) benchmark circuits. The results obtained are compared with the solutions derived from other stochastic algorithms and the proposed algorithm provides better solutions for both the benchmark circuits.

关键词： VLSI floorplanning particle swarm optimization algorithm Firefly algorithm Modified Corner List (MCL) algorithm Hybrid particle swarm optimization-Firefly algorithm (HPSOFF) non-slicing floorplan

来源：评论

学校读者我要写书评

暂无评论

Many-objective Reactive Power optimization Using particle swarm optimization algorithm Based on Pareto Entropy

Many-objective Reactive Power Optimization Using Particle Sw...

引用

IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Xu Zhuansun Zhu, Anming Wu, Jielong Han, Tong Chen, Yanbo Shaanxi Elect Power Co Postdoctoral Sci Res Stn Xian Peoples R China North China Elect Power Univ Sch Elect & Elect Engn Beijing Peoples R China

ISBN: (纸本)9781509054183

This paper presents a many-objective reactive power optimization model which consists of minimum active power loss, minimum node voltage deviation, maximum static voltage stability and maximum power supply capability. To efficiently solve this model, a novel approach by using particle swarm optimization is proposed. This approach is called manyobjective particle swarm optimization algorithm based on Pareto entropy which adopts loose Pareto dominant relationship and maps the Pareto front from cartesian coordinate system to parallel cell coordinate system, thus designing evolutionary strategies using Pareto front's distribution entropy and entropy difference in the new coordinate system. The presented algorithm is capable to balance convergence and diversity of the approximate Pareto front. Moreover, cell dominant intensity and individual density are introduced to assess the individual environment fitness of the Pareto optimal solution, and we hereby design the selection strategy of the global best solution. Simulations based on the IEEE 14-bus systems demonstrate the effectiveness of the proposed model and the efficiency of the proposed algorithm.

关键词： Many-objective reactive power optimization particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Evaluation of particle swarm optimization algorithm in Photovoltaic Applications 10

Evaluation of Particle Swarm Optimization Algorithm in Photo...

引用

10th International Conference on Intelligent Systems and Control (ISCO)

作者： Malarvizhi, E. Kamala, J. Sivasubramanian, A. St Josephs Coll Engn Dept ECE Madras Tamil Nadu India Anna Univ Dept ECE CEG Madras Tamil Nadu India VIT Univ Sch Elect Engn Madras Tamil Nadu India

ISBN: (纸本)9781467378079

Solar energy is the prime source of consumption for the world. It is the potential candidate for meeting the growing energy demand and solving environmental issues. To derive the maximum Power (MP) from the system, Maximum Power Point Tracking (MPPT) methods are implemented. It is highly essential to derive MP from the available solar energy. Over the years, numerous MPPT methods have been developed and presented in the literature. This paper discusses the evaluation of particle swarm optimization (PSO) algorithm in MPPT based solar power generation systems. It describes the different methodologies adopted to extract the maximum power from the solar array in photovoltaic (PV) power systems.

关键词： Photovoltaic power system Maximum Power Point Tracking particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

IMOPSO: An Improved Multi-objective particle swarm optimization algorithm 5

IMOPSO: An Improved Multi-objective Particle Swarm Optimizat...

引用

5th International Conference on Computer Science and Network Technology (ICCSNT)

作者： Ma, Borong Hua, Jun Ma, Zhixin Li, Xianbo Lanzhou Univ Sch Informat Sci & Engn Lanzhou Gansu Peoples R China

ISBN: (纸本)9781509021291

An improved multi-objective particle swarm optimization (IMOPSO) is presented because of the different demand for decision and state variables in engineering optimizations. IMOPSO adopts a new method of dynamic change about acceleration coefficients based on sine transform to improve the ability of global search in early period and the local search ability in the last runs of the algorithm. To expand the search area of particles, a drift motion is acted on the personal best positions. Moreover, a dynamic mutation strategy in which the mutation rates are generated by modified Levy flight is used to make the particles escape from the local optimal value. Finally, the efficiency of this algorithm is verified with test functions and the experimental results manifest that the IMOPSO is superior to MOPSO algorithm in wide perspectives like obtaining a better convergence to the true Pareto fronts with good diversity and uniformity.

关键词： particle swarm optimization algorithm Multi-objective optimization Acceleration coefficients Drift motion Mutation

来源：评论

学校读者我要写书评

暂无评论

Inverse Calculation for Thermal Conductivity of Soil particles Based on particle swarm optimization algorithm

引用

ELECTRONIC JOURNAL OF GEOTECHNICAL ENGINEERING 2016年第14期21卷 4613-4622页

作者： Jiang, Ping Xiao, Jingping Lu, Xilei Zeng, Hao Zhang, Fang Wang, Wei Shaoxing Univ Dept Bldg Engn Shaoxing Peoples R China Huahui Engn Design Grp Co Ltd Shaoxing Peoples R China Shaoxing Univ Dept Engn Management Shaoxing Peoples R China

Thermal conductivity is a significant parameter for studying the temperature effect of soil mechanical behaviours, and the thermal conductivity of soil particles has important influence on the calculation of soil thermal conductivity. As it is difficult to obtain the thermal conductivity of soil particles directly from test, it is usually obtained indirectly based on the inversion calculation of soil thermal conductivity. According to the characteristic of series parallel calculation prediction model of soil thermal conductivity, based on particle swarm optimization algorithm, the thermal conductivity of soil particles was inversely calculated by using the thermal conductivity of dry soil with different porosity;and the determination method of interpolation coefficient was also verified. The thermal conductivity of four kind's soil particles of peat silty clay, silty clay, gravel and gravel soil was inversely calculated to verify the accuracy of this model. The calculation results show that this prediction model can accurately determine the thermal conductivity of soil particles with a wide range of engineering applications

关键词： thermal conductivity series parallel model parametric inversion particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Research on Location Problem of Multi-distribution Center based on Chaos Adaptive Mutation particle swarm optimization algorithm

引用

INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING 2016年第12期9卷 283-294页

作者： Du, Tiaotiao Wu, Kaijun Wang, Tiejun LanZhou Jiao Tong Univ Sch Elect & Informat Engn Lanzhou 730070 Peoples R China Northwestern Univ National Sch Math & Comp Sci Inst Lanzhou 730030 Peoples R China

Location problem of multi-distribution center is a kind of NP hard problem. To solve such problems, this paper proposes a chaos adaptive mutation particle swarm optimization algorithm. The algorithm uses the ergodic property of chaos to initialize the particle swarm to enhance the diversity of the population, according to the variance of population fitness to adjust the probability of mutation, and adjust the inertia weight factor to improve the global and local search capability of the whole population. In this paper, the algorithm is applied to the location problem of multi-distribution center, established the multi-factor constraints of mathematical model which aiming at timeliness, and on this basis, the corresponding algorithm is designed. It can be seen from the location instance simulation results that the optimization results and efficiency of the adaptive mutation particle swarm optimization algorithm is better than the genetic algorithm and the standard particle swarm optimization algorithm.

关键词： Distribution center location particle swarm optimization algorithm Chaos adaptive mutation

来源：评论

学校读者我要写书评

暂无评论

Improved particle swarm optimization algorithm and its application in text feature selection

引用

APPLIED SOFT COMPUTING 2015年 35卷 629-636页

作者： Lu, Yonghe Liang, Minghui Ye, Zeyuan Cao, Lichao Sun Yat Sen Univ Sch Informat Management Guangzhou 510275 Guangdong Peoples R China

Text feature selection is an importance step in text classification and directly affects the classification performance. Classic feature selection methods mainly include document frequency (DF), information gain (IG), mutual information (MI), chi-square test (CHI). Theoretically, these methods are difficult to get improvement due to the deficiency of their mathematical models. In order to further improve effect of feature selection, many researches try to add intelligent optimization algorithms into feature selection method, such as improved ant colony algorithm and genetic algorithms, etc. Compared to the ant colony algorithm and genetic algorithms, particle swarm optimization algorithm (PSO) is simpler to implement and can find the optimal point quickly. Thus, this paper attempt to improve the effect of text feature selection through PSO. By analyzing current achievements of improved PSO and characteristic of classic feature selection methods, we have done many explorations in this paper. Above all, we selected the common PSO model, the two improved PSO models based respectively on functional inertia weight and constant constriction factor to optimize feature selection methods. Afterwards, according to constant constriction factor, we constructed a new functional constriction factor and added it into traditional PSO model. Finally, we proposed two improved PSO models based on both functional constriction factor and functional inertia weight, they are respectively the synchronously improved PSO model and the asynchronously improved PSO model. In our experiments, CHI was selected as the basic feature selection method. We improved CHI through using the six PSO models mentioned above. The experiment results and significance tests show that the asynchronously improved PSO model is the best one among all models both in the effect of text classification and in the stability of different dimensions. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Text classification Text feature selection particle swarm optimization algorithm Constriction factor

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：