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2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

作者： HAI-YANG JIA PENG YU JING-HUA GUAN DA-YOU LIU Sch.of Computer S&T Jilin University Postfach 13 00 12 Changchun China Key Lab.of Symbolic Computation & Knowledge Eng.of Ministry of Education Jilin University. Postfac

The purpose of this paper is to present and evaluate an improved Naive Bayes algorithm for clustering. Many researchers search for parameter values using EM *** is well-known that EM approach has a drawback-local optimal solution, so we propose a novel hybrid algorithm of the Discrete particle swarm optimization (DPSO) and the EM approach to improve the global search performance. We evaluate this hybrid approach on 4 real-world data sets from UCI repository. In a number of experiments and comparisons,the hybrid DPSO+EM algorithm exhibits a more effective and outperforms the EM approach.

关键词： Clustering Na(i)ve Bayes particle swarm optimization algorithm EM algorithm

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Network Intrusion Detection Analysis with Neural Network and particle swarm optimization algorithm

Network Intrusion Detection Analysis with Neural Network and...

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2010 Chinese Control and Decision Conference

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

Because the network intrusion behaviors are characterized with uncertainty,complexity and diversity,an intrusion detection method based on neural network and particle swarm optimization algorithm(PSOA) is presented in this *** novel structure model has higher accuracy and faster convergence *** construct the network structure,and give the algorithm *** discussed and analyzed the impact factor of intrusion *** the ability of strong self-learning and faster convergence,this intrusion detection method can detect various intrusion behaviors rapidly and effectively by learning the typical intrusion characteristic *** the character that rough set can keep the discern ability of original dataset after reduction,the reduces of the original dataset are calculated and used to train neural network,which increase the detection *** apply this technique on KDD99 data set and get satisfactory *** experimental result shows that this intrusion detection method is feasible and effective.

关键词： Neural Network Rough Set Network Intrusion Reduction 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...

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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： WEI-PING YANG Electric Engineering School Shanghai DianJi University Shanghai 200240 China

Vertical particle swarm optimization algorithm (VPSO) is proposed in this *** new algorithm assumes that the particles tend to fly towards two *** is flying toward the global best *** other is flying toward the vertical *** there is a random value produced in every iteration step to measure the probability of flying into two *** VPSO and particle swarm optimization algorithm (PSO) are used to train neural network (NN) and applied in soft-sensor of acrylonitrile ***, 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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Hybrid particle swarm optimization-Firefly algorithm (HPSOFF) for combinatorial optimization of non-slicing VLSI floorplanning

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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

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Study on Network Flow Prediction Model Based on particle swarm optimization algorithm and RBF Neural Network

Study on Network Flow Prediction Model Based on Particle Swa...

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2010 3rd IEEE International Conference on Computer Science and Information Technology—ICCSIT 2010

作者： Zhang Yu Bin HeBei Vocational Art College ShiJiaZhuang,China Lin Li Zhong ShiJiaZhuang College ShiJiaZhuang ,China Zhang Ya Ming ShiJiaZhuang Information Engineering Vocational College ShiJiaZhuang,China

It is significant to control network congestion by time series forecasting research for network flow. The hybrid method of particle swarm optimization algorithm and RBF neural network is applied to predict network flow and gain the desirable network flow prediction results. In the hybrid method, particle swarm optimization algorithm is selected and adjusted to the connection weights and the center of radial basis function and the width of radial basis function. The network flow data are collected to search the prediction ability of particle swarm optimization algorithm and RBF neural network. Compared with the results of RBF neural network and BP neural network, particle swarm optimization algorithm and RBF neural network has better forecasting performance.

关键词： network flow neural network particle swarm optimization algorithm prediction model

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Medical Image Registration Using particle swarm optimization algorithm with Mutation Operation

Medical Image Registration Using Particle Swarm Optimization...

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2008年国际电子商务、工程及科学领域的分布式计算和应用学术研讨会

作者： Zhijun Zhu Wenbo Xu Jun Sun School of Information Technology,Jiangnan University Wuxi,Jiangsu 214122,China

Image registration based on mutual information is of high accuracy and ***,it has received much attention these ***,the mutual information function is generally not a smooth function but one containing many local maxima,which has a large influence on *** has fewer parameters to control and can find the best solution quickly and guarantee to be global *** paper proposes a registration method based on wavelet *** this method the mutual information is used as the similarity measure and a hybrid algorithm combined by PSO algorithm and a mutation the search *** method is applied to the 2D registration of *** results show that this registration method could efficiently restrain local maxima of mutual information function and not only it can improve accuracy and speed but also the subvoxel accuracy can be achieved.

关键词： Image registration Mutual information particle swarm optimization algorithm Mutation Operation

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Multivariable PID Decoupling Control Method of Electroslag Remelting Process Based on Improved particle swarm optimization (PSO) algorithm

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INFORMATION 2014年第1期5卷 120-133页

作者： Wang, Jie-Sheng Ning, Chen-Xu Yang, Yang Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114044 Liaoning Peoples R China

A mathematical model of electroslag remelting (ESR) process is established based on its technical features and dynamic characteristics. A new multivariable self-tuning proportional-integral-derivative (PID) controller tuned optimally by an improved particle swarm optimization (IPSO) algorithm is proposed to control the two-input/two-output (TITO) ESR process. An adaptive chaotic migration mutation operator is used to tackle the particles trapped in the clustering field in order to enhance the diversity of the particles in the population, prevent premature convergence and improve the search efficiency of PSO algorithm. The simulation results show the feasibility and effectiveness of the proposed control method. The new method can overcome dynamic working conditions and coupling features of the system in a wide range, and it has strong robustness and adaptability.

关键词： electroslag remelting process multivariable system PID controller particle swarm optimization algorithm

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A particle-swarm-optimization-algorithm-Improved Jiles-Atherton Model for Magnetorheological Dampers Considering Magnetic Hysteresis Characteristics

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INFORMATION 2024年第2期15卷 101页

作者： Guo, Ying-Qing Li, Meng Yang, Yang Xu, Zhao-Dong Xie, Wen-Han Nanjing Forestry Univ Coll Mech & Elect Engn Nanjing 210037 Peoples R China Southeast Univ China Pakistan Belt & Rd Joint Lab Smart Disaster Nanjing 210096 Peoples R China

As a typical intelligent device, magnetorheological (MR) dampers have been widely applied in vibration control and mitigation. However, the inherent hysteresis characteristics of magnetic materials can cause significant time delays and fluctuations, affecting the controllability and damping performance of MR dampers. Most existing mathematical models have not considered the adverse effects of magnetic hysteresis characteristics, and this study aims to consider such effects in MR damper models. Based on the magnetic circuit analysis of MR dampers, the Jiles-Atherton (J-A) model is adopted to characterize the magnetic hysteresis properties. Then, a weight adaptive particle swarm optimization algorithm (PSO) is introduced to the J-A model for efficient parameter identifications of this model, in which the differential evolution and the Cauchy variation are combined to improve the diversity of the population and the ability to jump out of the local optimal solution. The results obtained from the improved J-A model are compared with the experimental data under different working conditions, and it shows that the proposed J-A model can accurately predict the damping performance of MR dampers with magnetic hysteresis characteristics.

关键词： magnetorheological damper Jiles-Atherton hysteresis model magnetic hysteresis characteristic particle swarm optimization algorithm

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Research on building truss design based on particle swarm intelligence optimization algorithm

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2022年第SUPPL 1期13卷 38-48页

作者： Sun, Yulong Li, Hongjuan Shabaz, Mohammad Sharma, Amit Yellow River Conservancy Tech Inst Kaifeng 075004 Henan Peoples R China Chitkara Univ Dept Comp Sci Engn Rajpura 140401 Punjab India

optimization methodologies are being utilized in various structural designing practices to solve size, shape and topology optimization problems. A heuristic particle swarm optimization (HPSO) algorithm was anticipated in this article in order to address the size optimization problem of truss with stress and displacement constraints. This article contributes in improvisation in the truss structure design rationality while reducing the engineering cost by proposing the HPSO approach. Primarily, the basic principle of the original PSO algorithm is presented, then the compression factor is established to improve the PSO algorithm, and a reasonable parameter setting value is presented. To validate the performance of the proposed optimization approach, various experimental illustrations were performed. The results show that the convergence history of experimental illustration 2 and experimental illustration 3 is optimal. The experimental illustration 2 converges after about 150 iterations, however, the experimental illustration 3 is close to the optimal solution after about 500 iterations. Therefore, the PSO algorithm can successfully optimize the size design of truss structures, and the algorithm is also time efficient. The improved PSO algorithm has good convergence and stability, and can effectively optimize the size design of truss structures.

关键词： particle swarm optimization algorithm Truss structure Compression factor Heuristic particle swarm optimization Engineering cost Topology optimization problems

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A Improved particle swarm optimization algorithm for the Application with Boundary-layer Problems of Power-law Fluids

A Improved Particle Swarm Optimization Algorithm for the App...

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2013 International Conference on Mechanical Engineering and Instrumentation(ICMEI 2013)

作者： Jianhui Zhou Lixi Yue Yannan Lu College of Qian'an Hebei United University

In fluid mechanics, how to solve power-law fluids in ordinary differential equations is always a concerned and difficult problem. we use generally a shooting method to tackle the boundary-layer problems under a suction/injection as well as a reverse flow boundary conditions. A improved particle swarm optimization algorithm(ISPO) is proposed for solving the parameter estimation problems of the multiple solutions in fluid mechanics. This algorithm has improved greatly in precision and the success rate. In this paper, multiple solutions can be found through changing accuracy and search coverage and multi-iterations of computer. Parameter estimation problems of the multiple solutions of ordinary differential equations are calculated, and the result has great accuracy and this method is practical.

关键词： particle swarm optimization algorithm Ordinary differential equations Parameter estimation boundary-layer problems

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