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检索条件"主题词=Particle Swarm optimization algorithm"

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Vertical particle swarm optimization algorithm and its application in soft-sensor modeling

Vertical particle swarm optimization algorithm and its appli...

引用

6th International Conference on Machine Learning and Cybernetics

作者： Yang, Wei-Ping Shanghai DianJi Univ Sch Elect Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9781424409723

Vertical particle swarm optimization algorithm (VPSO) is proposed in this paper. The new algorithm assumes that the particles tend to fly towards two directions. One is flying toward the global best particle. The other is flying toward the vertical direction. And there is a random value produced in every iteration step to measure the probability of flying into two directions. Both VPSO and particle swarm optimization algorithm (PSO) are used to train neural network (NN) and applied in soft-sensor of acrylonitrile yield. Finally, simulation results show that the method proposed by this paper is feasible and effective in soft-sensor of acrylonitrile yield.

关键词： particle swarm optimization algorithm optimization acrylonitrile soft-sensing

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Short-term load forecasting using artificial neural network based on particle swarm optimization algorithm

Short-term load forecasting using artificial neural network ...

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20th Annual Canadian Conference on Electrical and Computer Engineering

作者： Bashir, Z. A. El-Hawary, M. E. Dalhousie Univ Dept Elect & Comp Engn Halifax NS B3J 2X4 Canada

ISBN: (纸本)9781424410200

The quality of short term load forecasting can improve the efficiency of planning and operation of electric utilities. In this work, for determining the competitive learning model, the particle swarm optimization (PSO) technique is used as a training algorithm to adjust the weights of the artificial neural networks (ANNs) model to predict hourly loads. The feature of PSO is to fly potential solutions through hyperspace, accelerating toward better solutions. Thus the training phase should result in obtaining the weights configuration associated with the minimum output error. The historical load and weather information were trained and tested over a period of one season through two years. Generalized error estimation is done by using the reverse part of the data as a "test" set. The results were compared with conventional back-propagation algorithm and yielded encouraging results.

关键词： predicted hourly load neural networks particle swarm optimization algorithm

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Multi-threshold infrared image segmentation based on the modified particle swarm optimization algorithm

Multi-threshold infrared image segmentation based on the mod...

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6th International Conference on Machine Learning and Cybernetics

作者： Liu, Yi-Tong Fu, Ming-Yin Gao, Hong-Bin Beijing Inst Technol Sch Informat Sci & Technol Dept Automat Beijing 100081 Peoples R China

ISBN: (纸本)9781424409723

Threshold extraction is the fundamental step in multi-threshold image segmentation. This paper has introduced particle swarm optimization algorithm (PSO) for threshold extraction. But when dealing with the peaky high dimension function of maximum entropy for multi-threshold image segmentation, the conventional PSO is apt to be trapped in local optima called premature. This can cause image segmentation failure. This paper proposes a modified particle swarm optimization method (MPSO), which improves convergence speed and search capacity and avoid the premature phenomena when used in threshold extraction. Simulation results show that the MPSO has better performance and quicker speed. The experimental results also show that with the modified PSO as a threshold extraction method, the image is segmented fairly well and the segmentation speed improves greatly.

关键词： particle swarm optimization algorithm multi-threshold infrared image segmentation

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Application of particle swarm optimization algorithm to image texture classification

Application of particle swarm optimization algorithm to imag...

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Conference on Medical Imaging, Parallel Processing of Images, and optimization Techniques

作者： Ye, Zhiwei Zheng, Zhaobao Zhang, Jinping Yu, Xin Hubei Univ Technol Sch Comp Sci Wuhan 430068 Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China

ISBN: (纸本)9780819469533

This paper describes a kind of robust texture feature invariant to rotation and scale changes, which is the texture energy associated with a mask generated by particle swarm optimization algorithms. The detail procedure and algorithm to generate the mask is discussed in the paper. Furthermore, feature extraction experiments on aerial images are done. Experimental results indicate that the robust feature is effective and PSO-based algorithm is a viable approach for the "tuned" mask training problem.

关键词： particle swarm optimization algorithm feature extraction tuned mask

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optimization of PMSM Sensorless Control Based on Improved PSO algorithm

Optimization of PMSM Sensorless Control Based on Improved PS...

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International Conference on Manufacturing Science and Technology (ICMST 2011)

作者： Qian Miao-wang Tan Guo-jun Li Ning-Ning Zhao Zhong-xiang China Univ Min & Technol Sch Informat & Elect Engn Xuzhou Jiangsu Peoples R China Xuzhou Heavy Machinery Co Ltd Xuzhou Jiangsu Peoples R China

ISBN: (纸本)9783037852958

For the problem that manual adjustment of the parameters of controller in sensorless control system costs too much time, manpower and always can not get a good result, a new method based on improved particle swarm optimization algorithm is proposed to optimize the parameters. The improved algorithm is based on the standard particle swarm optimization with the simulated annealing algorithm and chaotic search brought in. The speed of motor is estimated by the extend Kalman filter. The error between measured speed and estimated speed of the permanent magnet synchronous motor rotor is used as the fitness function in order that the parameters in the covariance matrix is *** result of simulation indicates that high estimation precision can be got and the motor represents steadily with few of ripple of the actual *** this method, the time of adjustment is reduced and manpower is saved. In addition, the validity of the method is proved in experiment with dSPACE.

关键词： Permanent magnet synchronous motor sensorless control extend Kalman filter particle swarm optimization algorithm dSPACE

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An Intelligent Scheduling Strategy of Collaborative Logistics for Mass Customization

An Intelligent Scheduling Strategy of Collaborative Logistic...

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International Workshop on Information and Electronics Engineering (IWIEE) / International Conference on Information, Computing and Telecommunications (ICICT)

作者： Zhang, Min-Hui Gao, Xiao-Ling Zhou, Xin-Yi JianYang Sichuan Coll Educ Sch Comp Sci Chengdu 611130 Peoples R China

To improve the scheduling optimization of mass customization collaborative logistics, a scheduling solution developed based on the novel particle swarm optimization algorithm was proposed. A mathematic model based on scheduling strategy of mass customization logistics was designed. The novel dynamic particle swarm optimization algorithm framework was given. And simulation experiments were done to validate algorithm. Experiment results show that the proposed algorithm effectively improves the scheduling optimization of mass customization collaborative logistics, which has direct applications for Logistics scheduling (C) 2011 Published by Elsevier Ltd.

关键词： particle swarm optimization algorithm Mass customization collaborative logistics Logistic scheduling

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Two-Stage Multi-Objective Unit Commitment optimization under Future Load Uncertainty

Two-Stage Multi-Objective Unit Commitment Optimization under...

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6th International Conference on Genetic and Evolutionary Computing (ICGEC)

作者： Wang, Bo Li, You Watada, Junzo Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka 8080135 Japan

ISBN: (纸本)9780769547633;9781467321389

The unit commitment problem is to reduce the total generation cost as much as possible while satisfying future power demands. Therefore, optimization must be performed based on correct predictions of future demands. However, various uncertain factors affect these loads making an exact forecasting unsuccessful. This study mitigates this difficulty by applying fuzzy set theory and the objective is to build a two-stage multi-objective fuzzy programming model. To define the supply reliability effectively, we propose a new concept of maximal blackout time based on the fuzzy credibility theory. In addition, an improved two-layer multi-objective particle swarm optimization algorithm is designed as the solution. Finally, the performance of this study is discussed in comparison with experimental results from several test systems.

关键词： Load uncertainty Fuzzy set theory Two-stage multi-objective Maximal blackout time particle swarm optimization algorithm

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Intelligent optimization of hybrid wind/PV/pumped-storage power system

Intelligent optimization of hybrid wind/PV/pumped-storage po...

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1st International Conference on Energy and Environmental Protection (ICEEP 2012)

作者： Ren, Yan Zheng, Yuan Li, Chong Zhou, Bing Mao, Zhihao Hohai Univ Coll Energy & Elect Engn Nanjing 210098 Jiangsu Peoples R China N China Univ Water Conservancy & Elect Power Inst Elect Power Zhengzhou 450011 Peoples R China Nanjing Inst Technol Nanjing 211167 Peoples R China Patent Examinat Cooperat Ctr Sipo Beijing 1000910 Peoples R China Goupitan power plant Goupitan 564408 Guizhou Peoples R China

ISBN: (纸本)9783037854143

The hybrid wind/PV/pumped-storage power system was the hybrid system which combined hybrid wind/PV system and pumped-storage power station. System optimization was very important in the system design process. particle swarm optimization algorithm was a stochastic global optimization algorithm with good convergence and high accuracy, so it was used to optimize the hybrid system in this paper. First, the system reliability model was established. Second, the particle swarm optimization algorithm was used to optimize the system model in Nanjing. Finally, The results were analyzed and discussed. The optimization results showed that the optimal design method of wind/PV/pumped-storage system based on particle swarm optimization could take into account both the local optimization and the global optimization, which has good convergence high precision. The optimal system was that LPSP (loss of power supply probability) was zero.

关键词： hybrid wind/PV/pumped-storage power system particle swarm optimization algorithm intelligent optimization loss of power supply probability

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Optimal Relay Placement in Microgrids Considering Critical Clearing Time

Optimal Relay Placement in Microgrids Considering Critical C...

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IEEE International Power Engineering and optimization Conference

作者： S. J. Mirazimi B. Salehi M. Tadayon H. R. Karshenas Faculty of Engineering and Surveying University of Southern Queensland (USQ) Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Distribution Network Department Behrad Consulting Engineers Isfahan

ISBN: (纸本)9781467350723

This paper is concerned with the optimal placement of protection devices in a microgrid using particle swarm optimization algorithm. One of the main advantages of Distributed Generation (DG) scheme and microgrids in modern distribution systems is the reduction of number of outages and the associated damages caused by them. This task is accomplished by supplying a feeder from multiple sources. In order to prevent generator instability in DGs connected to utility, it is necessary to improve the protective schemes of traditional distribution systems and also to use proper relaying and setting for DGs. All of the downstream overcurrent (OC) relays of each DG are coordinated together and also should be coordinated with OC relay that is installed on the Point of Common Coupling (PCC) which is set at Critical Clearing Time (CCT) as a definite time, to have a desirable performance on each outage. In this paper, by the use of graph theory, various branches of a feeder are identified and the constraints for using particle swarm optimization algorithm to optimize the location of protective equipment are derived. In the proposed algorithm, the location, type and direction of relays are optimized simultaneously.

关键词： Microgrid particle swarm optimization algorithm Protection-Coordination Graph theory

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Parameter Identification Model with the Control Term in Batch Anaerobic Fermentation

Parameter Identification Model with the Control Term in Batc...

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2nd International Conference on Advanced Design and Manufacturing Engineering (ADME 2012)

作者： Li, Xiaohong Zhang, Shuxi Xu, Zhilong Feng, Enmin Liaoning Univ Sci & Technol Coll Sci Anshan 114051 Liaoning Peoples R China Dalian Univ Technol Sch Environm & Biol Sci & Technol Dalian 116024 Peoples R China Dalian Univ Technol Math Acad Sci Dalian 116024 Peoples R China

ISBN: (纸本)9783037855027

In this paper, according to the characteristics, dynamical behavior and the experimental data of the batch anaerobic culture, a parameter identification model was improved to describe the dynamical system for microorganism in batch fermentation. And some relative characters were introduced. Finally, a PSO algorithm with the inertia weight was used to get the best optimal parameter of the identification model. The results show that the model reduces the errors between the experimental data and computational values, and they can simulate the process of batch fermentation better.

关键词： batch fermentation parameter identification particle swarm optimization algorithm knetics system

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