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2009 2nd IEEE International Conference on Computer Science and Information Technology（ICCSIT 2009）

作者： WenJie Tian Automation Institute Beijing Union University,Beijing,China Yue Tian Finance Institute Capital University of Economics and Business Beijing,China Lan Ai Automation Institute Beijing Union University,Beijing,China JiCheng Liu Automation Institute Beijing Union University,Beijing,China

A novel structure of dynamic model is proposed in this paper and applied to construct a dynamic model to correct the dynamic errors of the infrared thermometer,because of which the dynamic performance of the thermometer is effectively *** dynamic compensator is established and the compensation is described and explicated by the Wiener *** to Wiener model,the novel structure is *** identification of thermometer non-linear dynamic compensator is achieved by particle swarm optimization *** results show that the stabilizing time of the thermometer is reduced less than 7 ms from 26 ms and the dynamic performance is obviously improved after compensation.

关键词： Wiener model compensation particle swarm optimization algorithm infrared thermometer identification

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Network Traffic Forecasting Based on Support Vector Machine Optimized by Keeping Activity particle swarm optimization algorithm

Network Traffic Forecasting Based on Support Vector Machine ...

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2015 International Conference on Advanced Educational Technology and Information Engineering(AETIE 2015)

作者： S.X.Xu X.J.Wang Xuchang Vocational Technical College

Keeping particle swarm alive support vector machine optimized algorithm network traffic forecasting model(EPSO-SVM) is proposed. First, building support vector machine learning sample by calculating the delay time and embedding dimension, second, learning network flow training set by using the maintaining the vitality of particle swarm optimization support vector machine, at last, validating performance EPSO-SVM's by using set of the network traffic tests. The results showed that the proposed model can improve the forecasting precision of network traffic. It has great practical application value.

关键词： particle swarm optimization algorithm vector machine forecast

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A Parallel particle swarm optimization algorithm

A Parallel Particle Swarm Optimization Algorithm

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2006年国际电子、工程及科学领域的分布式计算和应用学术研讨会

作者： Yan Ma,Jun Sun,Wenbo Xu Institute of Information Technology,Southern Yangtze University,WuXi,214122,China

particle swarm optimization algorithm is a newly proposed population -based *** efficient in many optimization problems,it may encounter the problem of premature convergence and computational time *** this paper,we attempt to introduce parallel mechanism into PSO and proposes PPSO(Parallel PSO) *** test the PPSO on four widely known benchmark functions and the experiment results show the efficiency and efficacy of PPSO.

关键词： particle swarm optimization algorithm parallel parallel particle swarm optimization function test

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Improved VRP Based on particle swarm optimization algorithm

Improved VRP Based on Particle Swarm Optimization Algorithm

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2006年国际电子、工程及科学领域的分布式计算和应用学术研讨会

作者： Zixia Chen Center for Research in Modern Business Zhejiang Gongshang University Hangzhou,Zhejiang,310035,China Youshi Xuan College of Computer and Information Engineering,Zhejiang Gongshang University Hangzhou,Zhejiang,310035,China

The Improved Vehicle Routing Problem(IVRP) and Location Allocation Problems(LAP) were considered synthetically. And a mathematic model about IVRP has been built *** purpose of this model was to minimize the total cost by determining the location of the warehouse,allocating appropriate nurnber of vehicles for the selected warehouse and finding the optimal routing for each *** the same time,Improved particle swarm optimization algorithm with Genetic algorithm and Simulated Annealing were applied to solve an example of the *** good convergence state of the global best solution has proved the IVRP model was correct and the algorithm applied to solve IVRP was effective.

关键词： IVRP Logistics model particle swarm optimization algorithm Genetic algorithm Simulated Annealing

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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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2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

作者： YI-TONG LIU MING-YIN FU HONG-BIN GAO Department of Automation School of Information science and technology Beijing Institute of Technology Beijing 100081 China

Threshold extraction is the fundamental step in multi-threshold image *** paper has introduced particle swarm optimization algorithm (PSO) for threshold *** 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 *** can cause image segmentation *** 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 *** results show that the MPSO has better performance and quicker *** 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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Design and experimental investigation on VL-MLI intended for half height (H-H) method to improve power quality using modified particle swarm optimization (MPSO) algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第6期42卷 5939-5956页

作者： Ramaraju, Satish Kumar Kaliannan, Thenmalar Joseph, Sheela Androse Kumaravel, Umadevi Albert, Johny Renoald Natarajan, Arun Vignesh Chellakutty, Gokul Prasad Sengunthar Coll Engn Dept Med Elect Engn Namakkal Tamil Nadu India Vivekanandha Coll Engn Women Dept EEE Namakkal Tamil Nadu India Kongu Engn Coll Dept EEE Perundurai Tamil Nadu India Sengunthar Engn Coll Dept EEE Namakkal Tamil Nadu India Gokaraju Rangaraju Inst Engn & Technol Dept ECE Hyderabad Telangana India SNS Coll Engn Dept ECE Coimbatore Tamil Nadu India

A Voltage lift performance is an excellent role to DC/DC conversion topology. The Voltage Lift Multilevel Inverter (VL-MLI) topology is suggested with minimal number of components compared to the conventional multilevel inverter (MLI). In this method, the Modified particle swarm optimization (MPSO) conveys a primary task for the VL-MLI using Half Height (H-H) method, it determine the required optimum switching angles to eliminate desired value of harmonics. The simulation circuit for fifteen level output uses single switch voltage-lift inverter fed with resistive and inductive loads (R & L load). The power quality is developed by voltage-lift multilevel inverter with minimized harmonics under the various Modulation Index (MI) while varied from 0.1 up to 1. The circuit is designed in a Field Programmable Gate Array (FPGA), which includes the MPSO rules for fast convergence to reduce the lower order harmonics and finds the best optimum switching angle values. To report this problem the H-H has implemented with MPSO to reduce minimum Total Harmonic Distortion (THD) for simulation circuit using Proteus 7.7 simulink tool. Due to the absence of multiple switches, filter and inductor element exposes for novelty of the proposed system. The comparative analysis has been carried-out with existing optimization and modulation methods.

关键词： Solar-Photovoltaic voltage lift-multilevel inverter particle swarm optimization algorithm half height field program gate array

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Support Vector Regression and particle swarm optimization algorithm for Intelligent Electronic Circuit Fault Diagnosis

Support Vector Regression and Particle Swarm Optimization Al...

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2009 2nd IEEE International Conference on Computer Science and Information Technology（ICCSIT 2009）

In the analysis of electronic circuit fault diagnosis based on support vector regression (SVR),irrelevant or correlated features in the samples could spoil the performance of the SVR classifier,leading to decrease of prediction *** order to solve the problems mentioned above,this paper used rough sets as a preprocessor of SVR to select a subset of input variables and employed the particle swarm optimization algorithm (PSOA) to optimize the parameters of ***,the proposed PSOA-SVR model that can automatically determine the optimal parameters was tested on the prediction of electronic circuit ***,we compared the proposed PSOA-SVR model with other artificial intelligence models of (BPN and fix-SVR).The experiment indicates that the proposed method is quite effective and ubiquitous.

关键词： fault diagnosis rough set particle swarm optimization algorithm support vector regression electronic circuit

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Deceptive Problems of particle swarm optimization algorithm and Improvement of Their Performance

Deceptive Problems of Particle Swarm Optimization Algorithm ...

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2010 International Colloquium on Computing,Communication, Control, and Management (CCCM2010)

作者： Shu Wang~1 1Department of Computer Science Yangzhou University Yangzhou,China Ling Chen~(1,2) 2State Key Lab of Novel Software Technology Nanjing University Nanjing,China

This paper introduces two instances of deceptive problems of particle swarm optimization(PSO).We theoretically prove that PSO can not converge to the global optimal solution of those two problems under certain *** addition,we empirically verified the correctness of our theoretical *** order to solve the two deceptive problems,we propose an algorithm ISA-PSO (inverse search area-PSO),which can change the particles' search direction and expand their search area at the appropriate *** experimental results show the effectiveness of our algorithm ISA-PSO.

关键词： particle swarm optimization algorithm Deceptive problems Convergence

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Study on Passenger Train Stopping Scheme Based on Improved particle swarm optimization algorithm

Study on Passenger Train Stopping Scheme Based on Improved P...

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2009 IEEE International Conference on Intelligent Computing and Intelligent Systems(ICIS2009)

作者： Shuang Wang,Peng Zhao,Ke Qiao School of Traffic and Transportation Beijing Jiaotong University Beijing,P.R.China

This paper develops a multi-objective optimization model for the passenger train stopping scheme on high-speed railway *** the stopping times for all passenger trains,minimizing travel distance of empty trains and minimizing the number of transfer passengers are the three planning objectives of the *** a given travel demand and specified capacity of stops,the model is solved by heuristic *** improved discrete particle swarm optimization(PSO) algorithm is presented to determine the best-compromise train stopping scheme with high effectiveness and *** the algorithm,a stop based representation is designed,and a new method is used to update the position and velocity of *** order to keep the particle swarm algorithm from premature stagnation,the simulated annealing algorithm,which has local search ability,is combined with the PSO algorithm to make elaborate search near the optimal solution,then the quality of solutions is improved *** empirical study on a given small railway network is conducted to demonstrate the effectiveness of the model and the performance of the *** experimental results show that the hybrid algorithm has great advantages in both success rate and convergence speed compared with other discrete PSO algorithm and genetic algorithm,and an optimal set of stopping schemes can always be generated for a given *** achieve the best planning outcome,the stopping schemes should be flexibly planned,and not constrained by specific ones as often set by the planner.

关键词： high-speed railway lines passenger train stopping scheme multi-objective programming particle swarm optimization algorithm simulated annealing algorithm

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An improved particle swarm optimization algorithm for AVO elastic parameter inversion problem

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2019年第9期31卷

作者： Wu, Qinghua Zhu, Zhixin Yan, Xuesong Gong, Wenyin Wuhan Inst Technol Fac Comp Sci & Engn Wuhan 430074 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China

The elastic parameter inversion technique for prestack seismic data, which combines the intelligent optimization algorithms with Amplitude Variation with Offset (AVO) technology, is an effective method for oil and gas exploration. However, when certain biological-evolution-based optimization algorithms, eg, genetic algorithms, are used to solve this problem, the computation exhibits fast convergence and a strong tendency to be trapped to a local optimum, thereby leading to unsatisfactory inversion results. To address this issue, this paper proposes a swarm-intelligence-based method-particle swarm optimization (PSO) algorithm to handle the elastic parameter inversion problem. Based on the Aki-Richards approximation to the Zoeppritz equations, the improved PSO algorithm adopts a special initialization strategy, which can enhance the smoothness of the initialization parametric curves. Extensive experimental research confirms the superiority of the proposed algorithm. Specifically, the improved PSO algorithm is able to not only markedly enhance inversion precision but also render remarkably high correlation coefficients associated with the elastic parameters.

关键词： correlation coefficient elastic parameters inversion particle swarm optimization algorithm prestack seismic

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