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A particle swarm optimization-based algorithm for finding gapped motifs

BIODATA MINING 2010年第1期3卷 1-12页

作者： Lei, Chengwei Ruan, Jianhua Univ Texas San Antonio Dept Comp Sci San Antonio TX 78249 USA

Background: Identifying approximately repeated patterns, or motifs, in DNA sequences from a set of co-regulated genes is an important step towards deciphering the complex gene regulatory networks and understanding gene functions. Results: In this work, we develop a novel motif finding algorithm (PSO+) using a population-based stochastic optimization technique called particle swarm optimization (PSO), which has been shown to be effective in optimizing difficult multidimensional problems in continuous domains. We propose a modification of the standard PSO algorithm to handle discrete values, such as characters in DNA sequences. The algorithm provides several features. First, we use both consensus and position-specific weight matrix representations in our algorithm, taking advantage of the efficiency of the former and the accuracy of the latter. Furthermore, many real motifs contain gaps, but the existing methods usually ignore them or assume a user know their exact locations and lengths, which is usually impractical for real applications. In comparison, our method models gaps explicitly, and provides an easy solution to find gapped motifs without any detailed knowledge of gaps. Our method allows the presence of input sequences containing zero or multiple binding sites. Conclusion: Experimental results on synthetic challenge problems as well as real biological sequences show that our method is both more efficient and more accurate than several existing algorithms, especially when gaps are present in the motifs.

关键词： particle swarm optimization particle swarm optimization algorithm Motif Finding Motif Length Real Motif

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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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Optimal Configuration of Micro-grids Power Supply Based on Operating Modes

Optimal Configuration of Micro-grids Power Supply Based on O...

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IEEE International Conference on Power and Renewable Energy (ICPRE)

作者： Lei Bin Yu Xueying Li Huaqiang Sichuan Elect Power Corp Power Econ Res Inst Chengdu Peoples R China Sichuan Univ Chengdu Peoples R China

ISBN: (纸本)9781509030682

The reasonable capacity configuration of distributed generation in a micro-grid is the prerequisite of its reliable and economical operation. This paper establishes a optimal configuration model of micro grid power supply aiming to reach the goal of the optimal comprehensive benefits of micro grid, and then solve the configuration model by using particle swarm optimization algorithm.

关键词： micro-grid comprehensive benefits power supply optimal configuration particle swarm optimization algorithm

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Power System Oscillation Damping by Intelligent Power System Stabilizer 1

Power System Oscillation Damping by Intelligent Power System...

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1st IEEE International Conference on Power Electronics, Intelligent Control, and Energy Systems (ICPEICES)

作者： Bhattacharya, Sragdhara Natl Inst Technol Patna Dept Elect Engn Patna Bihar India

ISBN: (纸本)9781467385879

Power Systems are inherently non-linear systems that are frequently subjected to various disturbances causing oscillations at low frequencies that may lead to instability. Generators are usually provided with power system stabilizers minimize the effect of these oscillations. The objective of this paper is find the optimal parameters for a conventional lead-lag compensator based Power System Stabilizer (PSS) for a system comprising of a generator connected to an infinite bus and containing a STIA type excitation system. The tuning of the parameters of the Power System Stabilizer is accomplished using the particle swarm optimization (PSO) algorithm. In this paper, a Fuzzy Power System Stabilizer (FPSS) where the optimal values of the parameters of the FPSS are decided using the PSO algorithm is also designed. The particle swarm optimization based conventional PSS and the particle swarm optimization based Fuzzy PSS are also incorporated in a system containing multiple machines to check the system responses under different loading conditions and faults of different types. The simulation results clearly prove the efficiency of the PSO based conventional and fuzzy power system stabilizers in damping the low frequency speed and power oscillations occurring in the power system due to various disturbances.

关键词： Fuzzy Logic based Power System Stabilizer Lead-lag Power System Stabilizer Multi-machine system Oscillation damping particle swarm optimization algorithm

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An Improved PSO-BP Neural Network and Its Application to Earthquake Prediction 28

An Improved PSO-BP Neural Network and Its Application to Ear...

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28th Chinese Control and Decision Conference

作者： Li, Cao Liu, Xiaoyu Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan 430081 Peoples R China

ISBN: (纸本)9781467397148

This paper presents a way of combining BP (Back Propagation) neural network and an improved PSO (particle swarm optimization) algorithm to predict the earthquake magnitude. It is known that the BP neural network and the normal PSO-BP neural network have some defeats, such as the slow convergence rate, easily falling into local minimum values. For improving the properties of PSO, some proposed the linear decreasing inertia weight strategy. Furthermore, this paper uses a nonlinear decreasing inertia weight in PSO to get a faster training speed and better optimal solutions. Compared with the linear decreasing strategy, the inertia weight in our nonlinear method has a faster declining speed in the early iteration, which can enhance the searching precision. In the late iteration, the inertia weight has a slower declining speed to avoid trapping in local minimum value. Then we apply the improved PSO to optimize the parameters of BP neural network. In the end, the improved PSO-BP neural network is applied to earthquake prediction. The simulation results show that the proposed improved PSO-BP neural network has faster convergence rate and better predictive effect than the BP neural network and the normal PSO-BP neural network.

关键词： BP neural network particle swarm optimization algorithm Inertia Weight Earthquake Prediction

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A Method Based on PSO for Pipe Routing Design

A Method Based on PSO for Pipe Routing Design

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6th lEEE Annual International Conference on Cyber Technalogy in Automation, Control and Intelligent Systems (IEEE-CYBER)

作者： Wang Changtao Sun Xiaotong Sun Liangliang Yuan Tiancheng Shenyang Jianzhu Univ Sch Informat & Control Engn Shenyang Peoples R China

ISBN: (纸本)9781509027323

The optimization of pipe path planning plays an important role in a building system. The three-dimensional building environment model is established, because of complicated building pipeline systems. Also this paper proposed a new method of pipe routing layout-design a quick method of generating orthogonal path. particle swarm optimization (PSO) is then used for optimizing the pipe path with a fixed-length code for the path, and for it's vulnerable to local problems, a mutation operator is adding to increase the diversity of the population. Finally, simulation results are presented and prove the feasibility and effectiveness of this method.

关键词： pipe path planning three-dimensional model particle swarm optimization algorithm fixed-length code

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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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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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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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Dynamic Hybrid swarm Intelligence Approach to Generate Fuzzy Rules

Dynamic Hybrid Swarm Intelligence Approach to Generate Fuzzy...

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2016 IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference(IMCEC 2016)

作者： Hai-xiao Chi Jin Gou Cheng Wang Zong-wen Fan College of Computer Science and Technology Huaqiao University

ISBN: (纸本)9781509001668

An important application field of swarm intelligence algorithms is fuzzy rule ***,their limitations are showed in two *** one hand,it takes a long process to create fuzzy rules during the iterations;on the other,the swarm intelligence algorithms obtain local optimal solution at *** overcome these disadvantages,a dynamic hybrid swarm intelligence approach is proposed to generate fuzzy rules from *** this approach,the dynamic adjustment strategy accelerates convergence rate of the swarm intelligence algorithms,meanwhile,the hybrid particles are introduced to avoid being trapped in the local *** adopt the Chinese Longitudinal Healthy Longevity Survey data to prove the effectiveness of the *** to the experiments,the dynamic hybrid swarm intelligence approach provides a competitive results comparing with the differential evolution algorithm,particle swarm optimization algorithm,social emotional optimization algorithm,and Wang Mendel method.

关键词： particle swarm optimization algorithm differential evolution algorithm social emotional optimization algorithm fuzzy rules Chinese healthy aging

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