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2009 IEEE International Conference on Intelligent Computing and Intelligent Systems(ICIS2009)

作者： WU Jianxin College of mechanical engineering Inner Mongolia University of Technology Hohhot China ZHAO WeiGuo College of mechanical engineering Inner Mongolia University of Technology Hohhot China HEXiangXin College of mechanical engineering Inner Mongolia University of Technology Hohhot China WANG Rui College of mechanical engineering Inner Mongolia University of Technology Hohhot China

Aiming at the electromechanical coupling system dynamics optimization of spindle unit of refitted machine tool for solid rocket,the optimization modeling is presented on the basis of system differential *** research job in the paper reveals that the global optimization efficiency can be enhanced greatly,when the weight value of the swarm particle algorithm can be changed with special exponential ***,a kind of new particle swarm algorithm,Exponential inertia weight particle swarm Optimization（EPSO）,is formed by adopting exponential inertia weight *** on above research job,the optimized design parameters of the spindle unit of refitted machine tool for solid rocket are obtained in limit time period,and the engineering problem of dynamic optimization of electromechanical system is solved successfully by the method of *** results are the innovative achievements in the field of mechatronics,and have broad application prospects in the design of robots,NC machine,and electromechanical equipments.

关键词： particle swarm algorithm electromechanical coupling system dynamics optimization

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An initial population optimization-based particle swarm algorithm for anti-jamming communication decision engines

An initial population optimization-based particle swarm algo...

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11th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM 2015)

作者： Xianyang Hui Jianzhong Chen Yingtao Niu ICE PLAUST Nanjing China Nanjing Telecommunication Technology Institute China

ISBN: (纸本)9781510821279

To improve the real-time performance of anti-jamming decision making, this paper proposes an initial population optimization particle swarm algorithm (IPO-PSO). This algorithm utilizes a portion of the solution from the previous decision as the initial population in the current decision; thus optimizes the adaptive quality of the initial population used for the current decision. The simulation results show that the proposed method can increase the convergence rate of the particle swarm optimization algorithm without increasing the complexity. Thus, this method provides impressive real-time performance and is ideally suited for demanding applications such as in military antijamming communications systems.

关键词： Anti-Jamming Communication Convergence Speed Decision Engine Initial Population Optimization particle swarm Optimization particle swarm algorithm Antijamming particle swarm optimization COMMUNICATION SYSTEMS Engines

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A Hybrid Multiobjective particle swarm Optimization algorithm Based on R2 Indicator

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IEEE ACCESS 2018年 6卷 14710-14721页

作者： Wei, Li-Xin Li, Xin Fan, Rui Sun, Hao Hu, Zi-Yu Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Hebei Peoples R China

When dealing with complex multiobjective problems, particle swarm optimization algorithm is easy to fall into local optimum and lead to uneven distribution. Therefore, this paper presents a hybrid multiobjective particle swarm optimization algorithm based on R2 indicator (R2HMOPSO) for solving multiobjective optimization problem. The proposed algorithm uses the sigmoid function mapping method to adjust the inertia weight and learning factors in order to tradeoffs the exploration and exploitation process effectively. In addition, simulation binary crossover operator is designed to reinitialize the particles to improve the search capability of the algorithm and to prevent particles from falling into local optimum and premature convergence. R2 indicator is incorporated into the R2HMOPSO algorithm so as to deal with the solutions of uneven distribution on the true Pareto front. Besides, polynomial mutation is used to maintain diversity in the external archive. The improved algorithm is evaluated on standard benchmarks. By comparing it with four state-of-the-art multiobjective optimization algorithms, the simulation results show that R2HMOPSO algorithm is competitive and effective in terms of convergence and distribution.

关键词： Multiobjective optimization problem R2 indicator particle swarm algorithm decomposition method

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Hull Coating Damage Location Prediction Using an Improved Annealing particle swarm Optimization algorithm

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CORROSION 2024年第9期80卷 939-947页

作者： Luo, Hui Liu, Bin Guo, Yujie Li, Fang Zhang, Jiangjiang Liu, Yingwei SINOPEC Northwest Co China Petr & Chem Corp Key Lab Enhanced Oil Recovery Carbonate Fractured Urumqi 830010 Peoples R China Xinjiang Univ Sch Mat Sci & Engn Urumqi 830046 Peoples R China Harbin Engn Univ Coll Mat Sci & Chem Engn Harbin 150001 Peoples R China

Damaged hull coatings can exacerbate corrosion to the ship body and timely detection and repair of defects are necessary to prevent further corrosion. Due to the large size of the ship, detection of the defects is not easy, especially those located below the waterline. When the impressed current cathodic protection detection system is combined with specialized software, this problem can be solved. This article has developed a software system that can predict damaged locations. The system adopts an improved annealing particle swarm optimization algorithm, combined with electrochemical theory, which can search for defects through iteration. Simulation results show that the system can locate the defects accurately in situations, where there are only one or two damaged parts with high accuracy.

关键词： damage prediction impressed current cathodic protection inverse analysis particle swarm algorithm ship hull

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Study on network security situation awareness based on particle swarm optimization algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2018年 125卷 764-775页

作者： Zhao Dongmei Liu Jinxing Hebei Normal Univ Sch Informat Technol Nanerhuan Rd Shijiazhuang 050024 Hebei Peoples R China Hebei Key Lab Network & Informat Secur Shijiazhuang 050024 Hebei Peoples R China First Aeronaut Coll Air Force Dept Airborne Weap Xinyang 464000 Peoples R China

The research of network security situation assessment has made great progress, but with the advent of the era of big data, the results of these studies show its limitations and shortcomings, it is difficult to meet the network security under the big data environment needs. Aiming at the network security situation awareness in large data environment, the network security situation awareness index system is firstly established, and the index factors are selected and quantified, and then the situation value is calculated to construct the network security situation awareness system. For the selection and quantification of index factors, we select multi-source data in large data environment, and propose a parallel reduction algorithm based on attribute importance matrix to reduce data source data attributes. For the calculation of the situation, the traditional wavelet neural network learning method is easy to fall into the local minimum, the wavelet neural network parameters are optimized by particle swarm algorithm, and then the wavelet neural network based on particle swarm optimization is applied to calculate the situation value. The simulation results show that the algorithm has fast convergence speed and good fitting effect.

关键词： Network security situation Factor extraction Attribute importance matrix Rough set particle swarm algorithm Wavelet neural network

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Intelligent transient calibration of a dual-loop EGR diesel engine using chaos-enhanced accelerated particle swarm optimization algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2019年第7期233卷 1698-1711页

作者： Zhang, Yunfan Zhou, Quan Li, Ziyang Li, Ji Xu, Hongming Univ Birmingham Sch Engn Dept Mech Engn Birmingham W Midlands England

This article proposes an intelligent transient calibration method for the air-path controller of a light-duty diesel engine. This method is developed based on the chaos-enhanced accelerated particle swarm optimization algorithm. The target is to reduce the engine's fuel consumption during transient scenarios by optimizing the controller parameters. The advanced dual-loop exhaust gas recirculation system is first introduced. Then, it formulates the transient calibration process as a multiple-objective optimization problem with constraints. Different from steady state calibration, the proposed method designs a new cost-function to evaluate the controller's transient performance. The intelligent transient calibration module is programmed in MATLAB code. Interface between the calibration module and a physical engine plant is established via ETAS INCA. The optimization result of the proposal method is discussed by comparing it with the result of existing calibration methods. The engine performance with the calibrated controller is evaluated based on engine tests.

关键词： Diesel engine multiple-objective optimization particle swarm algorithm transient calibration test bench validation

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A novel characterization method of piezoelectric composite material based on particle swarm optimization algorithm

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APPLIED MATHEMATICAL MODELLING 2019年 66卷 322-331页

作者： Sun, Xuan Li, Shiyang Dun, Xiangming Li, Deyue Li, Tangan Guo, Rongjing Yang, Ming Shanghai Jiao Tong Univ Shanghai Engn Res Ctr Intelligent Diag & Treatmen Dept Instrument Sci & Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Mech Engn Robot Inst Shanghai Peoples R China Shanghai Univ Dept Math Shanghai Peoples R China

This paper presents a novel characterization method for high-loss piezoelectric composite material based on particle swarm optimization algorithm. This proposed method was applied to determine the properties parameters of 1-3 PZT5A/epoxy composite piezoelectric material with the thickness vibration mode. The analysis results show that this method has more accurate reconstructed values, faster convergence speed compared to the method using simulated annealing algorithm published in the literature under the same condition. Good agreement between the measured electrical impedance curve and the fitting one also verifies that this method can determine precise materials parameters. This is very useful for the accurate characterization of piezoelectric materials with the unknown parameters. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Piezoelectric composite material particle swarm algorithm Simulated annealing algorithm Parameter characterization

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Improved Method for Acoustic Emission Source Location Based on DFB Laser Sensor and particle swarm Optimization algorithm

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IEEE SENSORS JOURNAL 2024年第12期24卷 19151-19158页

作者： Hu, Yunxin Chen, Chengyuan Han, Feng Chen, Xuefeng Guo, Ying Liu, Shengchun Heilongjiang Univ Heilongjiang Prov Key Lab Metamat Phys & Device Harbin 150080 Peoples R China

Acoustic emission (AE) location is important for finding the structural crack and ensuring the structural safety. Fiber laser sensors have ability to replace traditional piezoelectric sensors in acquiring AE source position information. In this article, improved AE source localization accuracy method based on distributed feedback (DFB) fiber laser sensor and particle swarm optimization (PSO) was proposed. Parameters, such as the effect of interferometer arm length difference and the directivity of the DFB sensor, were analyzed. Compare the results of hyperbolic localization algorithm and PSO algorithm for locating AE sources on plexiglass panels. The experimental results show that the particle swarm localization algorithm used in this article has a higher localization accuracy than the traditional hyperbolic localization algorithm. The localization error is reduced by 39% to 0.41 cm, which is expected to be applied in the field of building structural health.

关键词： Acoustic emission (AE) distributed feedback (DFB) fiber laser localization particle swarm algorithm

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A Fixture Assembly Process Planning Method Based on a particle swarm algorithm

A Fixture Assembly Process Planning Method Based on a Partic...

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2015 International Industrial Informatics and Computer Engineering Conference(IIICEC 2015)

作者： Yan Cao Yujia Wu Sen Cao School of Mechatronic Engineering Xi'an Technological University Shandong Vocational College of Economics and Business

In digital product assembly planning,different assembly process will direct affect the choice of assembly tools and fixtures,assembly efficiency and assembly *** application of a particle swarm algorithm to assembly process planning problem is studied in order to achieve the shortest completion *** the shortest completion time of all the machines as the optimization goal,an optimization model is *** particle swarm algorithm is realized in *** example is used to test the *** results show its effectiveness to solve assembly process planning problems.

关键词： particle swarm algorithm Optimization model Fixture Assembly process planning

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The algorithm Research of Adaptive Noise Cancellation Based on Dual Arrays and particle swarm algorithm

The Algorithm Research of Adaptive Noise Cancellation Based ...

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2012 International Conference on Environmental Engineering and Technology(ICEET2012)

作者： Tan Jingjing,Zhao Ping,Wang Wei,Wei Xu,Wei Yong School of Electronics and Information,Northwestern Polytechnical University,710072,Xi'an,Shanxi,P.R.China

In the complex application environment of fuzzy sound tracking,the source direction,amplitude and phase fuzzy problems are inevitably existed in signal processing from microphone arrays,so a kind of adaptive noise cancellation algorithm based on array particle swarm algorithm is proposed in this *** signal is collected through the beam-forming technology,particle swarm algorithm is applied in adaptive noise cancellation to solve the fuzzy *** simulation results show that this algorithm has better denoising ability and performance than the traditional method,SNR is also greatly improved,and the fuzzy problems of speech data can be solved effectively.

关键词： particle swarm algorithm Array beamforming Fuzzy information processing Adaptive noise canceller

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