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A particle swarm optimization-based algorithm for finding gapped motifs

BIODATA MINING 2010年第1期3卷 9-9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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Model of coal product structure based on particle swarm optimization algorithm

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Procedia Earth and Planetary Science 2009年第1期1卷 640-647页

作者： Zhang-guo, Wang Ya-li, Kuang Zhe, Lin Chang-sheng, Shi School of Chemical Engineering and Technology China University of Mining and Technology Xuzhou 221116 China Environmental Engineering Department North China Institute of Science and Technology Beijing 101601 China

Planning rational product structure of coal preparation is the key to attain the maximization of economic benefit in coal preparation enterprise and to save energy resources. There are many factors effect the preparation product structure, such as raw coal quality, separating methods, coal price, processing cost, product quality demands and equipment performance, etc. The research focuses on the optimization of product structure under the Multi-factor influences. In order to maximizing the economic benefit, the algorithm model of product structure is established, and the multiple influence factors are transformed as model parameters and constraint conditions. Then the particle swarm optimization (PSO) algorithm is used to search the optimal scheme of product structure. According to the actual requirement, the model was divided into several child models during the calculation. A set of practical software has been developed based on the research. The result shows that using PSO algorithm can get better convergence effect and avoid the local optimization for the Multi-factor model and that the optimal scheme of product structure from the model accord with the practical situation. © 2009.

关键词： particle swarm optimization (PSO) algorithms Coal deposits Coal industry Coal preparation Coal research Convergence of numerical methods Energy resources Industrial research Shape optimization Structural optimization algorithm model Child models Coal products Constraint conditions Economic benefits Equipment performance Gravity separation Influence factors Local optimizations Model optimization Model parameters Multi factor Optimal scheme particle swarm optimization algorithm Processing costs Product quality Product structure PSO algorithms Raw coal Save energy

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Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization

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APPLIED ACOUSTICS 2014年第0期80卷 57-67页

作者： Hajihassani, M. Armaghani, D. Jahed Sohaei, H. Mohamad, E. Tonnizam Marto, A. Univ Teknol Malaysia Dept Geotech & Transportat Fac Civil Engn Utm Skudai 81310 Johor Malaysia

Blasting is an inseparable part of the rock fragmentation process in hard rock mining. As an adverse and undesirable effect of blasting on surrounding areas, airblast-overpressure (AOp) is constantly considered by blast designers. AOp may impact the human and structures in adjacent to blasting area. Consequently, many attempts have been made to establish empirical correlations to predict and subsequently control the AOp. However, current correlations only investigate a few influential parameters, whereas there are many parameters in producing AOp. As a powerful function approximations, artificial neural networks (ANNs) can be utilized to simulate AOp. This paper presents a new approach based on hybrid ANN and particle swarm optimization (PSO) algorithm to predict AOp in quarry blasting. For this purpose, AOp and influential parameters were recorded from 62 blast operations in four granite quarry sites in Malaysia. Several models were trained and tested using collected data to determine the optimum model in which each model involved nine inputs, including the most influential parameters on AOp. In addition, two series of site factors were obtained using the power regression analyses. Findings show that presented PSO-based ANN model performs well in predicting the AOp. Hence, to compare the prediction performance of the PSO-based ANN model, the AOp was predicted using the current and proposed formulas. The training correlation coefficient equals to 0.94 suggests that the PSO-based ANN model outperforms the other predictive models. (c) 2014 Elsevier Ltd. All rights reserved.

关键词： Quarry blasting Airblast-overpressure Artificial neural networks particle swarm optimization algorithm

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Robust H-infinity Tracking Control Combined with Optimized PSS by PSO algorithm for Multimachine Power System

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JOURNAL OF ELECTRICAL SYSTEMS 2015年第1期11卷 36-48页

作者： Dib, Faiza Ben Meziane, Khaddouj Boumhidi, Ismail Sidi Mohamed Ben Abdellah Univ Fac Sci Dept Phys LESSI Lab Dhar Elmahrez Fez Morocco

In this paper, the proposed controller design is based on H-infinity tracking control combined with the optimized Power System Stabilizer (PSS). In addition the parameters of the PSS controller are optimized using the particle swarm optimization algorithm (PSO). The aim of this study is to obtain a high performance for the speed deviation and the angle rotor simultaneously, also the damping of the oscillations and the enhancing power system stability. Using the H-infinity tracking control show the convergence of the errors to the neighborhood of zero. In order to test the effectiveness of the proposed method, the simulation results clearly indicate the damping of the oscillations of the angle rotor and angular speed with reduced overshoots which confirms the performance of the proposed scheme.

关键词： H-infinity tracking control power system stabilizer particle swarm optimization algorithm multi machine power system

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The Study on optimization Design of Propeller Pitch

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JOURNAL OF COASTAL RESEARCH 2015年第Spe73期000卷 466-470页

作者： Wang, Chao Ye, Liyu Chang, Xin Huang, Sheng Harbin Engn Univ Coll Shipbldg Engn Harbin 150001 Peoples R China

With the increasing requirements of the hydrodynamic performance of the propeller, the optimization design of propeller has been gradually taken by people. With the DTRC series propellers as master model, this paper uses the theoretical prediction program based on surface panel method and combines with particle swarm optimization algorithm to study the optimization of propeller pitch (the other parameter is the same as the original propeller). In the optimization process, there are two different kinds of pitch expression (linear superposition method and Bezier function method) to fit radial distribution of pitch. With open water efficiency as the goal, the propeller is optimized and then discusses the influence of skew on open water efficiency. The result shows that Bezier method fits pitch curve more smoothly compared with hick-henne method, and that under the condition of meeting the thrust coefficient, the optimized propeller of Bezier method has higher open water efficiency.

关键词： Propeller hydrodynamic performance propeller pitch particle swarm optimization algorithm

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ANFIS Based Time Series Prediction Method of Bank Cash Flow Optimized by Adaptive Population Activity PSO algorithm

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INFORMATION 2015年第3期6卷 300-313页

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

In order to improve the accuracy and real-time of all kinds of information in the cash business, and solve the problem which accuracy and stability is not high of the data linkage between cash inventory forecasting and cash management information in the commercial bank, a hybrid learning algorithm is proposed based on adaptive population activity particle swarm optimization (APAPSO) algorithm combined with the least squares method (LMS) to optimize the adaptive network-based fuzzy inference system (ANFIS) model parameters. Through the introduction of metric function of population diversity to ensure the diversity of population and adaptive changes in inertia weight and learning factors, the optimization ability of the particle swarm optimization (PSO) algorithm is improved, which avoids the premature convergence problem of the PSO algorithm. The simulation comparison experiments are carried out with BP-LMS algorithm and standard PSO-LMS by adopting real commercial banks' cash flow data to verify the effectiveness of the proposed time series prediction of bank cash flow based on improved PSO-ANFIS optimization method. Simulation results show that the optimization speed is faster and the prediction accuracy is higher.

关键词： time series prediction bank cash flow adaptive network-based fuzzy inference system particle swarm optimization algorithm

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PSO-BASED TMD & ATMD CONTROL FOR HIGH-RISE STRUCTURE EXCITED BY SIMULATED FLUCTUATING WIND FIELD

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ENGINEERING REVIEW 2015年第3期35卷 247-257页

作者： Huang, W. Zhu, D. Y. Xu, J. Wang, X. J. Lu, J. W. Lu, K. L. Hefei Univ Technol Sch Civil & Hydraul Engn Hefei Peoples R China China Natl Machinery Ind Corp Beijing Peoples R China Anhui Prov Lab Civil Engn & Mat Hefei Peoples R China Hefei Univ Technol Sch Mech & Automot Engn Hefei Peoples R China

This paper reports a novel control strategy combined with artificial intelligence for wind-induced vibration control of a high-rise structure and also provides a broader idea for traditional structural vibration control. The fast Fourier transform based on decimation-in-time was used to optimize the waves with a weighted amplitude method, and the wind speed field was numerically generated according to a Davenport - type fluctuating wind speed spectrum. A second-generation benchmark structure was selected as the high-rise building model. Tuned mass damper (TMD) and active tuned mass damper (ATMD) served as the controller, and the linear-quadratic-Gaussian algorithm served as the active control algorithm for ATMD. Simultaneously, the particle swarm optimization algorithm was introduced, and the integral of the absolute value of the error based on the relative displacement of floors with regard to the ground level was defined as a performance index for optimizing. The numerical results reveal that both of the two proposed controllers have excellent capability in reducing wind-induced vibrations in high-rise buildings;moreover, the PSO-based ATMD performed better than PSO-based TMD.

关键词： Decimation-in-time Fast Fourier transform Waves with weighted amplitude Wind speed spectrum Benchmark structure Tuned mass damper Active tuned mass damper Linear quadratic Gaussian algorithm Integral of the absolute value of the error particle swarm optimization algorithm

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Optimal parameters selection for BP neural network based on particle swarm optimization: A case study of wind speed forecasting

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KNOWLEDGE-BASED SYSTEMS 2014年第0期56卷 226-239页

作者： Ren, Chao An, Ning Wang, Jianzhou Li, Lian Hu, Bin Shang, Duo Lanzhou Univ Sch Math & Stat Lanzhou 730000 Gansu Peoples R China Lanzhou Univ Sch Informat Sci & Engn Adv Comp Lab Lanzhou 730000 Gansu Peoples R China Hefei Univ Technol Gerontechnol Lab Hefei Anhui Peoples R China Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Gansu Peoples R China SUNY Stony Brook Coll Engn & Appl Sci Stony Brook NY 11794 USA

As a clean and renewable energy source, wind energy has been increasingly gaining global attention. Wind speed forecast is of great significance for wind energy domain: planning and design of wind farms, wind farm operation control, wind power prediction, power grid operation scheduling, and more. Many wind speed forecasting algorithms have been proposed to improve prediction accuracy. Few of them, however, have studied how to select input parameters carefully to achieve desired results. After introducing a Back Propagation neural network based on particle Swam optimization (PSO-BP), this paper details a method called IS-PSO-BP that combines PSO-BP with comprehensive parameter selection. The IS-PSO-BP is short for Input parameter Selection (IS)-PSO-BP, where IS stands for Input parameter Selection. To evaluate the forecast performance of proposed approach, this paper uses daily average wind speed data of Jiuquan and 6-hourly wind speed data of Yumen, Gansu of China from 2001 to 2006 as a case study. The experiment results clearly show that for these two particular datasets, the proposed method achieves much better forecast performance than the basic back propagation neural network and ARIMA model. (C) 2013 Elsevier B.V. All rights reserved.

关键词： BP neural network Input parameters selection particle swarm optimization algorithm Wind speed Wind forecasting

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Intrusion Detection Quantitative Analysis with Support Vector Regression and particle swarm optimization algorithm

Intrusion Detection Quantitative Analysis with Support Vecto...

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International Conference on Wireless Networks and Information Systems

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

ISBN: (纸本)9780769539010

Because the network intrusion behaviors are characterized with uncertainty, complexity and diversity, a new method based on support vector regression (SVR) and particle swarm optimization algorithm (PSOA) is presented and used for pattern analysis of intrusion detection in this paper. The novel structure model has higher accuracy and faster convergence speed. We construct the network structure, and give the algorithm flow. We discussed and analyzed the impact factor of intrusion behaviors. With 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 information. We use rough set to reduce dimension. We apply this technique on KDD99 data set and get satisfactory results. The experimental result shows that this intrusion detection method is feasible and effective.

关键词： support vector regression intrusion detection particle swarm optimization algorithm rough set convergence

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A particle swarm optimization algorithm Based on Molecule Diffusion

A Particle Swarm Optimization Algorithm Based on Molecule Di...

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International Conference on Industrial Mechatronics and Automation

作者： Liu, Xiaoxiang Jiang, Weigang Xie, Jianwen Jinan Univ Zhuhai Coll Dept Comp Sci Zhuhai Peoples R China

ISBN: (纸本)9781424438181

Inspired by the diffusion movement phenomenon of the molecule, a molecule-diffusion particle swarm optimization (MDPSO) is presented. The proposed algorithm (MDPSO) has attraction and diffusion phases. Once the diversity of population become low, the individuals will be dispersed and turn into diffusion phases, while if the diversity of population get high, the individuals carry out the attraction phases. It is indicated that MDPSO not only prevents premature convergence to a high degree, but also keeps a more rapid convergence rate than SPSO by applying MDPSO to portfolio problem and comparing with SPSO and other algorithms.

关键词： molecule diffusion particle swarm optimization algorithm portfolio selection

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