检索结果-内蒙古大学图书馆

2012 International Conference on Computer Technology and Science(ICCTS 2012)

作者： Li Li1,Wang Keqi2,and Yue Qi3 1,3 Information and Computer Engineering College,Northeast Forestry University,Harbin,China 2 Forestry Engineering Automation Discipline,Northeast Forestry University,Harbin,China

In order to exert the advantage of ant colony algorithm and particle swarm optimization algorithm respectively,a method combined the two algorithms was designed for solving multi-objective flexible job shop scheduling problem in this *** proposed algorithm was composed by two *** first phase made use of the fast convergence of PSO to search the particles optimum position and made the position as the start point of *** the second phase,the traditional ant colony algorithm was improved and was used to search the global optimum scheduling according to its characters of positive feedback and structure of solution *** combined algorithm was validated by practical *** results obtained have shown the proposed approach is feasible and effective for the multi-objective flexible job shop scheduling problem.

关键词： multi-objective flexible job shop scheduling particle swarm optimization algorithm ant colony algorithm

来源：评论

学校读者我要写书评

暂无评论

Adaptive Weighted Hybrid PSOGSA algorithm and its Application in Power System Economic Load Dispatch Problem

Adaptive Weighted Hybrid PSOGSA Algorithm and its Applicatio...

引用

第32届中国控制与决策会议

作者： Xingcheng Pu Longlong Zhao Chaowen Xiong Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing University of Posts and Telecommunications School of Automation Chongqing University of Posts and Telecommunications

Gravity Search algorithm（GSA） is a swarm intelligence optimization algorithm based on the gravity *** standard GSA algorithm has strong global search capability,while its convergence speed is *** particle swarm optimization（PSO） algorithm has high convergence speed and search *** on the advantages of the above two algorithms,a hybrid algorithm（PSOGSA） is proposed in this paper,and two adaptive weighted update strategies are introduced into the optimization process to improve the search accuracy of the hybrid *** the same time,we added variable mutation probability to solve the problem that particles are easily be trapped in local *** order to verify the effectiveness of the two improved hybrid algorithms,the two algorithms are applied to the power system economic load dispatch（ELD） *** generation cost optimization performance tests are computed for three groups of power systems with different unit *** simulation results show that the two adaptive weighted hybrid algorithms which are proposed in this paper can effectively reduce the generation cost of the power system.

关键词： Adaptive Weighted Power System Economic Load Dispatch Problem particle swarm optimization algorithm Gravity Search algorithm

来源：评论

学校读者我要写书评

暂无评论

A Quantum particle Swam optimization algorithm with Available Transfer Capability

A Quantum Particle Swam Optimization Algorithm with Availabl...

引用

第十九届分布式计算及其应用国际学术研讨会

作者： Qu Liping Li Dongheng Meng Yan XUE Hai-bo Engineering Training Center of Beihua University Beihua University State Grid Jilin Electric Power Co. Ltd.Shulan power supply company Department of Electrical Engineering Jilin Institute of industrial technology Peoples Government of Jilin City

Quantum particle swarm algorithm integrated the quantum behavior with particle swarm optimization algorithm,is used to settle the majorization question of calculating available transmission *** 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

Fuzzy Comprehensive Evaluation for Transmission Grid Planning Based on Combination Weight

Fuzzy Comprehensive Evaluation for Transmission Grid Plannin...

引用

2014 International Conference on Power and Energy(ICPE 2014)

作者： Z.H.LI Y Lin W.H.Chen D.H.You G.Wang M.L.Dong Q.R.Chen Y.Zou J.Hu Guangdong Power Grid Development Research Institute Co. Ltd State Key Laboratory of Advanced Electromagnetic Engineering and Technology of Huazhong University of Science and Technology

In order to remedy the defect that the determination of weight in fuzzy comprehensive evaluation is impacted by subjectivity, the combination weight is introduced. In this paper, the optimization Model combines the subjective weight and objective weight, which is solved by PSO, is built. At the same time, In order to give full consideration to the uncertain and fuzzy factors which involved in the grid planning, this paper combines the combination weight and fuzzy comprehensive evaluation to assess the grid-planning scheme. The calculation results of IEEE Garver-6 show that the proposed fuzzy comprehensive evaluation system is clear in theory and convenient to calculate, the combination weight is objective and reasonable as well as its results are scientific and intuitive.

关键词： grid planning scheme fuzzy comprehensive evaluation combination weight particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Optimal Active Power Loss with Feeder Routing Collaborate Distributed Generation Allocation and Sizing in Smart Grid Distribution

Optimal Active Power Loss with Feeder Routing Collaborate Di...

引用

2017经济金融与统计国际会议

作者： Phatcharasak Phawanaphinyo Narongdech Keeratipranon Chaiyaporn Khemapatapan College of Innovative Technology and Engineering Dhurakij Pundit University

The Smart Grid(SG) has widely supported the electric power from the Distributed Generation(DG). It becomes a practical standard to generate the electric power from a renewable energy into the distribution system to compensate for the power demand, especially in the peak time. However, the advancement of the SG continuing with the classical problem of active power loss as the traditional grid. This research aimsto solve the active power loss problem by analyzing the elements and study to solve, in the scope of the organization that provides the electrical power. In order to solve the problem, the solutions can be achieved by feeder routing with adjusting cost Dijkstra's algorithm, afterward decided the allocation and sizing of DG by using the Evolutionary Computing(EC) which are Harmony Search(HS), Artificial Bee Colony(ABC), and particle swarm optimization(PSO) algorithms. The experiment evaluates the performance of the algorithm usingpower flow analysis, Backward/Forward Sweep Method, on the IEEE 33-bus system. From the experimental result, the PSO provides the best performance. The overall active power loss in the case 3 DGs was reduced from 202.67 to 52.29 k W, representing 74.20%reduction.

关键词： Minimal active power loss Backward\Forward Sweep Method Harmony Search algorithm Artificial Bee Colony algorithm particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Design and optimization of Control Parameters Based on Direct-Drive Permanent Magnet Synchronous Generator for Wind Power System

Design and Optimization of Control Parameters Based on Direc...

引用

IEEE Conference on Industrial Electronics and Applications

作者： Lixia Sun Chengya Gong Fengling Han College of Energy and Electrical Engineering Hohai university Nanjing School of Computer Science and Information Technology

ISBN: (纸本)9781467363204

The direct-drive permanent magnet synchronous generator (DDPMSG) for wind power system uses a back-to-back double PWM converter. PI controller based on decoupling control strategies is used to control generator side converter and grid side converter. But the parameters of the PI controller are difficult to obtain correctly. Though manual tuning method is applied to regulate the parameters, the method would waste a lot of time and greatly depend on the experience. The paper analyses the mathematical model of direct-drive permanent magnet synchronous wind power generation system. It presents a particle swarm optimization (PSO) method for determining the parameters of PI controller for PMSG to improve the control ability. PSO is powerful stochastic evolutionary algorithm that is used to find the global optimum solution in search space. Under the condition of wind speed mutation, the simulation results of PMSG system after PI parameter optimization show that the PI control with PSO algorithm can fit the real value. The PSO controller has fast convergence rate, strong adaptability and good dynamic performance.

关键词： PMSG Manual Tuning Method particle swarm optimization algorithm Parameter optimization

来源：评论

学校读者我要写书评

暂无评论

AN EFFECTIVE METHOD FOR RFID TAG ANTENNA optimization BASED ON ARTIFICAL NEURAL NETWORK

AN EFFECTIVE METHOD FOR RFID TAG ANTENNA OPTIMIZATION BASED ...

引用

2010 IEEE Youth Conference on Information,Computing and Telecommunications(2010 IEEE青年信息、计算和通信技术大会)

作者： Jiachuan Shang Ning Zhang Xiuping Li Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Millimeter Waves 210096 Nanjing

Evolutionary algorithms combined with artificial neural network (ANN) have been applied in RFID tag antenna optimization platform. An effective method for RFID tag antenna optimization by particle swarm optimization (PSO) algorithm or genetic algorithm (GA) combined with ANN is presented in this paper. ANN is used to establish the non-linear model of tag antenna which is shown to be as accurate as an electromagnetic simulator and can be used for constructing the fitness function of PSO and GA. The PSO and GA optimizers are developed and executed in C++. Finally, this optimization method is turned out to be much more efficient than any electromagnetic simulator optimization. In addition, the PSO optimization results show that it is faster than GA.

关键词： optimization Tag Antennas particle swarm optimization algorithm Genetic algorithm Neural Network

来源：评论

学校读者我要写书评

暂无评论

Based on PSO-BP Network algorithm for Fault Diagnosis of Power Transformer

Based on PSO-BP Network Algorithm for Fault Diagnosis of Pow...

引用

2010 International Conference on Computer,Mechatronics,Control and Electronic Engineering(2010计算机、机电、控制与电子工程国际会议 CMCE 2010)

作者： Hairu Li DaowuYang ZhuoRen ZhewenLi School of Chemical and Biological Engineering Changsha University of Science and Technology Changsha Ultrahigh Voltage Power Transmission and Transformer Company Changsha 410100 P.R.China

ISBN: (纸本)9781424479573

Dissolved gas analysis is an effective method for the early detection of incipient fault in power transformers. To improve the capability of interpreting the result of dissolved gas analysis, a technology is proposed in this paper. The particle swarm optimization (PSO) technique is used to integrate with Back Propagation (BP) neural networks, and using particle swarm to optimize the network's weights and biases, the fault of transformers is simulated and discussed. The results show that the accuracy of PSO-BP method is significantly higher than that of the conventional three-ratio method. So the algorithm based on PSO-BP network model provides a more accurate, safe and reliable result for the fault diagnosis of transformers.

关键词： component transformer fault diagnosis dissolved gas-in-oil analysis particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Short Term Load Forecasting and Early Warning of Charging Station Based on PSO-SVM

Short Term Load Forecasting and Early Warning of Charging St...

引用

作者： Liao Xiaoqun Kang Xiaofan Ma Li Cao Nanlan Xi’an University of Science & Technology

In this paper,a short-term load forecasting model and a load early warning model for charging station based on PSO-SVM are *** swarm optimization(PSO) is used to optimize the parameters of support vector machine(SVM) model,and the PSO-SVM load forecasting model for the optimal nuclear parameters of charging station is established according to the normalized root mean square error(NRMS).On the basis of it,a load warning model of charging station is established and verified by an *** show that the short-term load forecasting model based on PSO-SVM and the load forecasting model of charging station meet the requirements of forecasting and forecasting accuracy.

关键词： Load Forecasting Optimal Kernel Parameters Support Vector Machine particle swarm optimization algorithm Load Early Warning

来源：评论

学校读者我要写书评

暂无评论

Establishing an emergency communication network and optimal path using multiple autonomous rover robots

引用

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2019年第14期31卷 e4636-e4636页

作者： Sakthitharan, S. Jayashri, S. Adhiparasakthi Engn Coll Fac Informat & Commun Engn Melmaruvathur 603319 India

The natural calamity or disaster may destroy all communication networks especially a cellular network that relies on a tower. Although many solutions to an ad hoc wireless network have been proposed, forming a network covering a respective region with mobile robots toward optimal coverage remains to be an open problem. In this paper, we take the initiative to handle the optimal network coverage and path selection in disaster region with the help of multiple movable/rover robots. This paper consists of load balance distribution algorithm and optimal coverage algorithm applied to find the next optimally possible node location for all robots. Next, the robots maneuvering in an unknown disaster environment to identify the optimal path between the source and destination by using a particle swarm optimization algorithm. Finally, simulated results show that the algorithms can significantly improve the network coverage in the entire region, and the optimal path can effectively identify the optimal solution for all rover robots.

关键词： autonomous maneuvering disaster management load balancing algorithm optimal coverage algorithm 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

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：