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International Conference on Machine Learning and Cybernetics

作者： Zhou, Tie-Jun Li, Yang Li, Jia Hunan Univ Sch Comp & Commun Changsha 410082 Hunan Peoples R China Cent South Univ Forestry & Technol Coll Comp Sci Changsha 410004 Peoples R China

ISBN: (纸本)9781424447053

Intrusion detection plays more important role in network security today. This paper introduces a method, particle swarm optimization and support vector machine, to intrusion detection system, and presents a new design of ID Based on particle swarm optimization and Support Vector Machine. This paper presents an optimal selection approach of the SVM parameters(regulation parameter C and the radial basis function width parameter sigma) based on particle swarm optimization algorithm. The experiments show that the optimal parameter selection approach based on PSO is available and the Research of Intrusion Detection Based on particle swarm optimization and Support Vector Machine is effective in reducing the number of alerts, false positive, false negative better.

关键词： Intrusion detection particle swarm optimization algorithm Support vector machine Parameter optimization

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A Novel optimization Method for the Maximum Coverage Sets of WSN

A Novel Optimization Method for the Maximum Coverage Sets of...

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International Conference on Wireless Networks and Information Systems

作者： Tian, WenJie Liu, JiCheng Beijing Union Univ Automat Inst Beijing 100101 Peoples R China

ISBN: (纸本)9780769539010

To resolve the problem of traditional lifetime, target coverage and network connectivity, a novel algorithm for selecting the optimal coverage set based on improved particle swarm optimization algorithm (PSOA) is proposed. There are two competing objectives presented to determine where to place the sensor nodes, the coverage rate and the number of working nodes. And then As another new contribution, we apply the novel algorithm in the K-disjoint coverage sets problem, which divides all the sensors into K-disjoint sets, guaranteeing each set with complete coverage. This method can improve the capability of search and convergence of algorithm. By alternating coverage subsets and using only one at each round, the maximum network lifetime is achieved. The simulation result shows that our analysis for wireless sensor networks is better than other algorithms and more effective.

关键词： wireless sensor networks network lifetime target coverage particle swarm optimization algorithm K-disjoint sets

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Weak Signal Detection Based On Stochastic Resonance Combining With PSO algorithm

Weak Signal Detection Based On Stochastic Resonance Combinin...

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4th IEEE Conference on Industrial Electronics and Applications

作者： Hou, Zhefei Yang, Jie Wang, Kecheng Wang, Yunpeng Wuhan Univ Technol Sch Informat Engn Wuhan 430063 Peoples R China Air Force Dalian Commun Sergeant Sch Dalian 116600 Peoples R China

ISBN: (纸本)9781424427994

In order to detect a weak signal under the condition of intensive noise, the signal and additive white noise were used as input of a bistable stochastic resonance (SR) system. The noise intensity and the system parameters were adjusted adaptively with particle swarm optimization (PSO) algorithm by examining the SR effect on output signal-to-noise ratio (SNR). An improved numerical solution for a bistable SR model based on a fourth order Runge-Kutta algorithm was presented to enhance the SR effect. The simulation results show that the weak signal in an intensive noisy background could be successfully extracted. What is more, the output SNR was increased more than 20 dB comparing with the input SNR. The proposed approach was used to process the vibration signals of roller bearings to find the small faults in an early stage. The result showed that the approach satisfactorily extracts the defect characteristics. It can be seen that the proposed method was superior to the traditional spectra analysis and wavelet transform methods. Such detection approach indicates a promising prospect for mechanical fault monitoring and diagnosis.

关键词： signal detection non-linear bistable system stochastic resonance particle swarm optimization algorithm

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Dynamic Scheduling of Multi-task for Hybrid Flow-shop Based on Idle Time Windows

Dynamic Scheduling of Multi-task for Hybrid Flow-shop Based ...

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21st Chinese Control and Decision Conference

作者： Zeng Ling-li Zou Feng-xing Gao Zheng Xu Xiao-hong Natl Univ Def Technol Coll Mechatron & Automat Dept Automat Control Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781424427239

This paper presents an approach based on idle time windows (ITWs) and particle swarm optimization (PSO) algorithm to solve dynamic scheduling of multi-task for hybrid flow-shop. The idea of ITW is introduced, then the dynamic updating rules of the sets of ITWs are explained in detail. With the sets of ITWs of machines as constraints, the mathematical model is presented for dynamic scheduling of multi-task for hybrid flow-shop. The PSO algorithm is proposed in order to solve this problem. The results of simulation indicate that this approach satisfies the demand of dynamic scheduling of multi-task.

关键词： Idle Time Window Hybrid Flow-shop Multi-task Dynamic Scheduling particle swarm optimization algorithm

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Modeling and Forecasting Method Based on Support Vector Regression

Modeling and Forecasting Method Based on Support Vector Regr...

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2nd International Conference on Future Information Technology and Management Engineering

作者： Tian, WenJie Wang, ManYi Beijing Union Univ Automat Inst Beijing Peoples R China Capital Univ Econ & Business Finance Inst Beijing Peoples R China

ISBN: (纸本)9780769538808

In the predicting financial distress, we know that irrelevant or correlated features in the samples could spoil the performance of the SVR classifier, leading to decrease of prediction accuracy. In order to solve the problems mentioned above, this paper use rough sets as a preprocessor of SVR to select a subset of input variables and employ the particle swarm optimization algorithm (PSOA) to optimize the parameters of SVR. The proposed PSOA-SVR model can automatically determine the optimal parameters. This model is tested on the prediction of financial distress. Then, we compare 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： financial distress rough set particle swarm optimization algorithm support vector regression prediction

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Optimum Distribution of Aseismic Engineering Investment for Gas Pipeline Network System Based on Improved particle swarm algorithm

Optimum Distribution of Aseismic Engineering Investment for ...

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2nd International Conference on Intelligent Computation Technology and Automation

作者： Liu Wu Liu Yi Gu Xue-qin Wang Deng-hai Southwest Petr Univ Sch Petr Engn Chengdu Peoples R China Xian Changqing Technol Engn Co LTD Xian Peoples R China Southwest Petr Univ Sch Electron & Informat Engn Chengdu Peoples R China

ISBN: (纸本)9780769538044

Under the limit of engineering investment, the aseismic optimum design of pipeline system is used to minimize the sum of aseismic cost. Since optimal problem is an nonlinear hybrid model which involves dual "curse of dimensionality" during the mathematical programming and system reliability calculation, traditional optimal techniques can hardly solve the problems of distribution of aseismic engineering investment for lifeline network system. Evolutionary-based particle swarm algorithm has been investigated and the improved design of inertia weight has been fulfilled in this paper. A new hybrid particle swarm algorithm makes use of the ergodicity of chaos to improve the capability of precise search and keep the balance between the global search and the local search. Combining with Boolean cubical matrix disjoint calculation method used as aseismic reliability analysis tool of network system, the improved algorithm is applied to the optimization solution of investment distribution for gas pipeline network The analysis of an example in a certain pipeline network system demonstrates that the established model and algorithm can achieve better convergent performance and speed.

关键词： gas pipeline network aseismic optimum design distribution of investment particle swarm optimization algorithm inertia weight chaos

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swarm intelligence optimization and its application in geophysical data inversion

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Applied Geophysics 2009年第2期6卷 166-174页

作者： Yuan Sanyi Wang Shangxu Tian Nan CNPC Key Laboratory of Geophysical Exploration Key Laboratory of Earth Prospecting and Information Technology China University of Petroleum Beijing 102249 China

The inversions of complex geophysical data always solve multi-parameter, nonlinear, and multimodal optimization problems. Searching for the optimal inversion solutions is similar to the social behavior observed in swarms such as birds and ants when searching for food. In this article, first the particle swarm optimization algorithm was described in detail, and ant colony algorithm improved. Then the methods were applied to three different kinds of geophysical inversion problems：（1） a linear problem which is sensitive to noise, （2） a synchronous inversion of linear and nonlinear problems, and （3） a nonlinear problem. The results validate their feasibility and efficiency. Compared with the conventional genetic algorithm and simulated annealing, they have the advantages of higher convergence speed and accuracy. Compared with the quasi-Newton method and Levenberg-Marquardt method, they work better with the ability to overcome the locally optimal solutions.

关键词： swarm intelligence optimization geophysical inversion multimodal particle swarm optimization algorithm

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Optimal Multiuser MIMO Linear Precoding with LMMSE Receiver

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2009年第unknown期2009卷 197682-197682页

作者： Fang Shu Wu Gang Li Shao-Qian Univ Elect Sci & Technol China Natl Key Lab Commun Chengdu 611731 Peoples R China

The adoption of multiple antennas both at the transmitter and the receiver will explore additional spatial resources to provide substantial gain in system throughput with the spatial division multiple access (SDMA) technique. Optimal multiuser MIMO linear precoding is considered as a key issue in the area of multiuser MIMO research. The challenge in such multiuser system is designing the precoding vector to maximize the system capacity. An optimal multiuser MIMO linear precoding scheme with LMMSE detection based on particle swarm optimization is proposed in this paper. The proposed scheme aims to maximize the system capacity of multiuser MIMO system with linear precoding and linear detection. This paper explores a simplified function to solve the optimal problem. With the adoption of particle swarm optimization algorithm, the optimal linear precoding vector could be easily searched according to the simplified function. The proposed scheme provides significant performance improvement comparing to the multiuser MIMO linear precoding scheme based on channel block diagonalization method. Copyright (C) 2009 Fang Shu et al.

关键词： particle swarm optimization particle swarm optimization algorithm Minimum Mean Square Error MIMO Channel Linear Minimum Mean Square Error

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A Distributed Approach to Solving Hybrid Flow-shop Scheduling Problem

A Distributed Approach to Solving Hybrid Flow-shop Schedulin...

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21st Chinese Control and Decision Conference

作者： Zou Feng-xing Zeng Ling-li Gao Zheng Liu Feng Natl Univ Def Technol Dept Automat Control Coll Mechatron & Automat Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781424427239

In this paper, a mathematical programming model is established for hybrid flow-shop scheduling problem,with the minimum of the makespan as the objective function. Based on the particle swarm optimization algorithm, a distributed approach according to the process is presented to solve the global problem. Compared with the references, the experimental results indicate that the distributed approach performs better on improving computing and searching speed and being feasible and effective on global optimum.

关键词： Hybrid Flow-shop Scheduling Distributed Approach particle swarm optimization algorithm Time Consumption

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RESEARCH ON INTRUSION DETECTION OF SVM BASED ON PSO

RESEARCH ON INTRUSION DETECTION OF SVM BASED ON PSO

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2009 International Conference on Machine Learning and Cybernetics(2009机器学习与控制论国际会议)

作者： TIE-JUN ZHOU JIA LI YANG LI School of Computer and Communication Hunan University Changsha 410082 China Computer Science College Central South University of Forestry and Technology Changsha 410004 Chi

Intrusion detection plays more important role in network security today. This paper introduces a method, particle swarm optimization and support vector machine, to intrusion detection system, and presents a new design of ID Based on particle swarm optimization and Support Vector Machine. This paper presents an optimal selection approach of the SVM parameters(regulation parameter C and the radial basis function width parameter σ ) based on particle swarm optimization algorithm. The experiments show that the optimal parameter selection approach based on PSO is available and the Research of Intrusion Detection Based on particle swarm optimization and Support Vector Machine is effective in reducing the number of alerts, false positive, false negative better.

关键词： Intrusion detection particle swarm optimization algorithm Support vector machine Parameter optimization

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