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2008 International Seminar on Future BioMedical Information Engineering(FBIE 2008)

作者： Yi Liu Institute of Management Science& Information Engineering,Hangzhou Dianzi University,zhejiang 310018,China

Logistics distribution locating problem is an important area in Logistics,which select the most reasonable location of distribution centers from many *** paper establish the Cellular PSO algorithm,which combine the particle swarm optimization algorithm and cellular *** algorithm was tested in the simulation experiment,and the result indicate that the Cellular PSO algorithm is a effective method of solving the problem of choosing the distribution centers location which can overcome the low precision of the basic particle swarm optimization *** additional,the Cellular PSO algorithm has high quality and efficiency of searching.

关键词： Cellular Automata particle swarm optimization algorithm ,Logistics Distribution Centers Location

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

Fault Diagnosis Analysis with Support Vector Regression and ...

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The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

作者： WenJie Tian JiCheng Liu Beijing Automation Institute of Beijing Union University Beijing China 100101

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

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Performance comparison of particle swarm optimization and genetic algorithm for inverse surface radiation problem

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2015年 88卷 330-337页

作者： Lee, Kyun Ho Kim, Ki Wan Sejong Univ Dept Aerosp Engn Seoul 143741 South Korea Agcy Def Dev Daejeon 305600 South Korea

The heat transfer mechanism of thermal radiation is directly related to either the emission and propagation of electromagnetic waves or the transport of photons. Depending on the participation of the medium in space, thermal radiation can be classified into two forms, which are surface and gas radiation, respectively. In the present study, unknown surface radiation properties are estimated by an inverse analysis for a surface radiation in an axisymmetric cylindrical enclosure. For efficiency, the repulsive particle swarm optimization (RPSO) algorithm, which showed an outstanding effectiveness in the previous inverse gas radiation problem, is adopted as an inverse solver. By comparing the convergence rates of an objective function and the estimated accuracies with the results of the hybrid genetic algorithm (HGA) and the particle swarm optimization (PSO) method, the performance of the RPSO algorithm is verified to be quite an efficient method as the inverse solver when applied to the retrieval of unknown properties of the surface radiation problem. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Surface radiation Inverse analysis particle swarm optimization algorithm Genetic algorithm

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Database Query optimization based on Hybrid Variable particle swarm optimization algorithm

Database Query Optimization based on Hybrid Variable Particl...

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2014年国际计算机科学与软件工程学术会议

作者： Fen CHEN Yan TANG Fan-Ping ZHANG Suqian College Department of Computer Science SuqianJiangsuChina Computer and Information Engineering Hohai UniversityNanjingJiangsuChian College of Water Conservancy and Hydropower Engineering Hohai UniversityJiangsuChina

Query optimization is a Key research topic in the database research area aiming at solving the problem of premature convergence and local optimal trap in the traditional particle swarm optimization algorithm,this Paper Proposed a novel database query optimization algorithm called Hybrid Variable particle swarm optimization (HV-PSO);Firstly,database query optimization mathematical model is established,then the optimal solution is found by using information transferring and sharing mechanism of *** research has two novelties which contribute to the literature: *** charge of particle inertia weight in the optimization process to accelerate the convergence;*** introduction of "hybrid" variation operator to increase the diversity of ***,the simulation experiments are carried out to test the performance of *** results show that the HV-PSO could solve the deficiency of the traditional particle swarm optimization algorithm,not only improving the database query efficiency,but also obtaining better query ***,it has predominant advantage for querying large relational connections.

关键词： Database query particle swarm optimization algorithm Dynamic inertial weigh Multi-Joint Query Hybrid mutation Query plan

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Adaptive Weight particle swarm optimization algorithm with Constriction Factor

Adaptive Weight Particle Swarm Optimization Algorithm with C...

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2010 International Conference of Informationa Science and Management Engineering(2010年信息科学与管理工程国际学术会议 ISME 2010)

作者： Zhiyu You Weirong Chen Guojun He Xiaoqiang Nan School of Electrical Engineering Southwest Jiaotong University Chengdu China

In order to overcome the shortage of premature convergence caused by local optimization in the process of global optimization, an adaptive weight particle swarm optimization algorithm with constriction factor is proposed combined with an analysis of convergence of particle swarm optimization algorithm. The value of the inertia weight is set according to dynamic information about the changes in the objective function value, as to effectively balance the advantages of global optimization against the shortage of local optimization. Four Benchmark function are used for performance test of five different kinds of optimization algorithm, the final results shows that the proposed method has a good ability to slow down the pace of premature convergence, compared to other improved particle swarm algorithm.

关键词： particle swarm optimization algorithm convergence adaptive weight constriction factor

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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...

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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

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A Novel Quantum-behaved particle swarm optimization algorithm

A Novel Quantum-behaved Particle Swarm Optimization Algorith...

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第十四届分布式计算及其应用国际学术研讨会

作者： Jing Zhao Hong Liu School of Information Qilu University of Technology Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Shandong Normal University Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology College of Information Science and Engineering Shandong Normal University

A novel Quantum-behaved particle swarm optimization algorithm with probability(P-QPSO) is introduced to improve the global convergence property of QPSO. In the proposed algorithm, all the particles keep the original evolution with large probability, and do not update the position of particles with small probability, and re-initialize the position of particles with small probability. Seven benchmark functions are used to test the performance of P-QPSO. The results of experiment show that the proposed technique can increase diversity of population and converge more rapidly than other evolutionary computation methods.

关键词： particle swarm optimization algorithm quantum-behaved probability benchmark function

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An Intelligent Parameter Selection Method for particle swarm optimization algorithm

An Intelligent Parameter Selection Method for Particle Swarm...

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The Fourth International Joint Conference on Computational Science and optimization(第四届计算科学与优化国际大会 CSO 2011)

作者： Yuntao Dai Ying Li Liqiang Liu College of Science Harbin Engineering University Heilongjiang China College of Automation Harbin Engineering University Heilongjiang China

For the problem of particle swarm optimization parameters selection, a kind of intelligent method to optimum parameters selection using another particle swarm optimization algorithm is proposed. Firstly it analyzes the effect of each parameter on algorithm performance in detail. Then it takes parameter selection of PSO algorithm as a complex optimization problem, sets appropriate fitness function to describe optimization performance, and uses PSO-PARA algorithm to optimize the parameters selection method of PSO-OPT algorithm. Tests to the benchmark function show that these parameters are better than the experience parameters test results in the optimal fitness, the mean value of optimal fitness, convergence rate.

关键词： parameter optimization particle swarm optimization algorithm analysis of parameters

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An improved particle swarm optimization algorithm for P300 Neural network recognizer 23

An improved particle swarm optimization algorithm for P300 N...

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Proceedings of the 2023 13th International Conference on Communication and Network Security

作者： Wei Zou Shanshan Tu Zhenhu Ning Jiawei Xu Shuaikun Xing Xue Liao Department of Computer Science Beijing University of Technology China

ISBN: (纸本)9798400707964

P300 is an important control system signal in the brain, so there is an urgent need and practical significance to work on the efficient classification of P300 event-related potentials. In this article, we design a convolutional neural network CNNnet based on chaotic adaptive particle swarm optimization (CAPSO) algorithm for efficient and accurate detection and classification of P300 EEG signals. The chaotic adaptive particle swarm optimization algorithm uses Logistic chaotic mapping to initialize the initial position of particles, and adopts a dynamic adaptive weighting strategy. Compared with traditional particle swarm optimization algorithms, it can effectively improve the optimization speed and convergence speed of particles. The experimental results show that compared with other P300 detection neural networks and traditional particle swarm optimization algorithms, this algorithm has faster convergence speed and higher convergence accuracy, and can effectively avoid the problem of particle swarm falling into local optima.

关键词： P300 detection adaptive inertia weights chaotic mapping convolutional neural network particle swarm optimization algorithm

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Traffic Prediction Model Based on particle swarm optimization algorithm

Traffic Prediction Model Based on Particle Swarm Optimizatio...

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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

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