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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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Bluetooth Based Chaos Synchronization Using particle swarm optimization and Its Applications to Image Encryption

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SENSORS 2012年第6期12卷 7468-7484页

作者： Yau, Her-Terng Hung, Tzu-Hsiang Hsieh, Chia-Chun Natl Chin Yi Univ Technol Dept Elect Engn Taichung 41170 Taiwan

This study used the complex dynamic characteristics of chaotic systems and Bluetooth to explore the topic of wireless chaotic communication secrecy and develop a communication security system. The PID controller for chaos synchronization control was applied, and the optimum parameters of this PID controller were obtained using a particle swarm optimization (PSO) algorithm. Bluetooth was used to realize wireless transmissions, and a chaotic wireless communication security system was developed in the design concept of a chaotic communication security system. The experimental results show that this scheme can be used successfully in image encryption.

关键词： chaotic system Bluetooth communications security particle swarm optimization algorithm PID controller

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A Novel Hybrid optimization algorithm Based on Multi-agent and particle swarm

A Novel Hybrid Optimization Algorithm Based on Multi-agent a...

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International Conference on Components, Packaging and Manufacturing Technology

作者： Shi Dejia Jiang Weijin Ding Xiaoling Hunan Univ Commerce Sch Comp & Elect Engn Changsha 410205 Hunan Peoples R China

ISBN: (纸本)9780878492138

A novel multi-agent particle swarm optimization algorithm (MAI'SO) is proposed for optimal reactive power dispatch and voltage control of power system. The method integrates multi-agent system (MAS) and particle swarm optimization algorithm (PSO). An agent in *** represents a particle to PSO and a candidate solution to the optimization problem. All agents live in a lattice-like environment, with each agent fixed on a lattice-point. In order to decrease fitness value, quickly, agents compete and cooperate with their neighbors. and they can also use knowledge. Making use of these agent interactions and evolution mechanism of ***. MAPSO realizes the purpose of minimizing the value of objective function. MAPSO applied for optimal reactive power is evaluated on an IEEE 30-bus power system. It is shown that the proposed approach converges to better solutions much faster than the earlier reported approaches

关键词： Hybrid optimization algorithm multi-agent system particle swarm optimization algorithm

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Comparison and application of four versions of particle swarm optimization algorithms in the sequence optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第7期38卷 8858-8864页

作者： Zhang, Wei-Bo Zhu, Guang-Yu Fuzhou Univ Coll Mech Engn & Automat Fuzhou 35002 Fujian Peoples R China

particle swarm optimization (PSO) algorithm is a well-known optimization approach to deal with discrete problems. There are two models proposed for the operators of PSO algorithm, one is based on value exchange and the other on order exchange, accordingly two versions of PSO algorithms are formed. A new version of PSO algorithm based on order exchange has been presented in our studies, which is capable of converging on the global optimization solution, with the method of generating the stop evolution particle over again. In this paper, we propose another version of PSO algorithm based on value exchange with the same method. There exist, thus, totally four versions of PSO algorithms, which is given a brief introduction individually and the performance of which are compared in solving sequence optimization problems through fifty runs. The performance comparison show that the PSO algorithm with global convergence characteristics based on order exchange outperforms the other versions of PSO in solving sequence optimization problem. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： particle swarm optimization algorithm Sequence optimization Global convergence Hole machining Drilling

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Engine Idle Speed Control Using Nonlinear Model Predictive Control

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IFAC Proceedings Volumes 2013年第21期46卷 171-176页

作者： F. Xu H. Chen X. Gong Y.F. Hu State Key Laboratory of Automotive Simulation and Control Jilin University Changchun 130025 P.R. China Department of Control Science and Engineering Jilin University Changchun 130025 P.R. China

In this paper, a nonlinear model predictive controller is presented for idle speed control (ISC) problem of spark ignition (SI) engine. The objective is to maintain the engine speed at a prescribed set-point through actuating a electronic throttle, and minimize the effects of load torque disturbances and model uncertainties. The nonlinear programming (NLP) problem formed by nonlinear model predictive control (NMPC) is solved by using particle swarm optimization (PSO) algorithm. Simulation results show that the designed nonlinear model predictive controller can achieve satisfactory performance for ISC.

关键词： Engine idle speed control nonlinear model predictive control nonlinear programming problem uncertainty particle swarm optimization algorithm

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Pass Design and optimization Based on Finite Element Modeling

Pass Design and Optimization Based on Finite Element Modelin...

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32nd Chinese Control Conference (CCC)

作者： Xiao Cheng Wu Min Chen Xin Jin Feng Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China Hunan Engn Lab Adv Control & Intelligent Automat Changsha 410083 Hunan Peoples R China Northeast Power Co Ltd Shenyang 110181 Peoples R China

ISBN: (纸本)9781479900305

The deformation of seamless steel pipe rolling process is complex due to material and geometrical nonlinearity, so it's hard to design pass and optimize rolling schedule. With the analysis of rolling for PQF, a method of pass design and optimization method based on the finite element modeling is presented. On the basis of pass design, the finite element simulation model is established. Considering the checking condition of pass design and rack load balanced allocation, the paper establishes an appraisal model based on the finite element modeling to evaluate pass design. Finally, as the evaluation function serves as the objective function, the paper uses particle swarm optimization algorithm to optimize specific elongation. These not only develop new products but also enhance the state rolling process characteristics (dimensional accuracy and internal and external surface quality).

关键词： Pass design Finite element modeling Appraisal model particle swarm optimization algorithm Elongation

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Design and optimization of Control Parameters Based on Direct-Drive Permanent Magnet Synchronous Generator for Wind Power System

Design and Optimization of Control Parameters Based on Direc...

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IEEE Conference on Industrial Electronics and Applications

作者： Lixia Sun Chengya Gong Fengling Han College of Energy and Electrical Engineering Hohai university Nanjing School of Computer Science and Information Technology

ISBN: (纸本)9781467363204

The direct-drive permanent magnet synchronous generator (DDPMSG) for wind power system uses a back-to-back double PWM converter. PI controller based on decoupling control strategies is used to control generator side converter and grid side converter. But the parameters of the PI controller are difficult to obtain correctly. Though manual tuning method is applied to regulate the parameters, the method would waste a lot of time and greatly depend on the experience. The paper analyses the mathematical model of direct-drive permanent magnet synchronous wind power generation system. It presents a particle swarm optimization (PSO) method for determining the parameters of PI controller for PMSG to improve the control ability. PSO is powerful stochastic evolutionary algorithm that is used to find the global optimum solution in search space. Under the condition of wind speed mutation, the simulation results of PMSG system after PI parameter optimization show that the PI control with PSO algorithm can fit the real value. The PSO controller has fast convergence rate, strong adaptability and good dynamic performance.

关键词： PMSG Manual Tuning Method particle swarm optimization algorithm Parameter optimization

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Fast Convergence particle swarm optimization for Functions optimization

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Procedia Technology 2012年 4卷 319-324页

作者： Amaresh Sahu Sushanta Kumar Panigrahi Sabyasachi Pattnaik Siksha ‘O’ Anusandhan University Bhubaneswar Orissa India Fakir Mohan University Balasore Orissa India

Time to time, many researchers have suggested modifications to the standard particle swarm optimization to find good solutions faster than the evolutionary algorithms, but they could be possibly stuck in poor region or diverge to unstable situations. For overcoming such problems, this paper proposes new Fast Convergence particle swarm optimization (FCPSO) approach based on balancing the diversity of location of individual particle by introducing a new parameter, particle mean dimension (Pmd) of all particles to improve the performance of PSO. The FCPSO method is tested with five benchmark functions by variable dimensions and fixed size population and compared with PSO & Constriction factor approach of PSO (CPSO). Finally, search performances of these methods on the benchmark functions are tested.

关键词： particle swarm optimization algorithm adaptive weight constriction factor particle mean dimension

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Nodes organization for channel assignment with topology preservation in multi-radio wireless mesh networks

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AD HOC NETWORKS 2012年第5期10卷 760-773页

作者： Cheng, Hongju Xiong, Naixue Vasilakos, Athanasios V. Yang, Laurence Tianruo Chen, Guolong Zhuang, Xiaofang Fuzhou Univ Coll Math & Comp Sci Fuzhou 350002 Peoples R China Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Univ Western Macedonia Dept Comp & Telecommun Engn Thessaloniki Greece St Francis Xavier Univ Dept Comp Sci Antigonish NS B2G 1C0 Canada

The wireless mesh network is a new emerging broadband technology providing the last-mile Internet access for mobile users by exploiting the advantage of multiple radios and multiple channels. The throughput improvement of the network relies heavily on the utilizing the orthogonal channels. However, an improper channel assignment scheme may lead to network partition or links failure. In this paper we consider the assignment strategy with topology preservation by organizing the mesh nodes with available channels, and aim at minimizing the co-channel interference in the network. The channel assignment with the topology preservation is proved to be NP-hard and to find the optimized solution in polynomial time is impossible. We have formulated a channel assignment algorithm named as DPSO-CA which is based on the discrete particle swarm optimization and can be used to find the approximate optimized solution. We have shown that our algorithm can be easily extended to the case with uneven traffic load in the network. The impact of radio utilization during the channel assignment process is discussed too. Extensive simulation results have demonstrated that our algorithm has good performance in both dense and sparse networks compared with related works. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Wireless mesh networks Multiple radios Multiple channels Channel assignment particle swarm optimization algorithm

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A multi-criteria decision support for optimal instrumentation in scoliosis spine surgery

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STRUCTURAL AND MULTIDISCIPLINARY optimization 2012年第6期45卷 917-929页

作者： de Oliveira, Marcelo Elias Hasler, Carol-Claudius Zheng, Guoyan Studer, Daniel Schneider, Jacques Buechler, Philippe Univ Bern Inst Surg Technol & Biomech CH-3014 Bern Switzerland UKBB Univ Childrens Hosp Beider Basel CH-4056 Basel Switzerland

In adolescent idiopathic scoliosis, the selection of an optimal instrumentation configuration for correcting a specific spinal deformity is a challenging combinatorial problem. Current methods mostly rely on surgeons' expertise, which has been shown to lead to different treatment strategies for the same patients. In this work, a mathematical model of the human spine derived from in-vitro experimentally-obtained data was used to simulate the biomechanical behavior of the spine under the application of corrective forces and torques. The corrective forces and torques were optimized based on the particle swarm optimization algorithm for each combinatorially possible instrumentation strategy. Finally, a multi-criteria decision support for optimal instrumentation in scoliosis spine surgery has been proposed and applied to five patient data sets exhibiting similar spinal deformities according to two commonly used classification systems. Results indicated that the classification of the spinal deformities based on the current standardized clinical classifications systems is not a sufficient condition for recommending selective fusion of spinal motion segments. In addition, the particle swarm optimization algorithm was successfully applied to solve a realistic interdisciplinary clinical problem in a patient-specific fashion. The proposed method enables a better understanding of the biomechanical behavior of spinal structures and has the potential to become a standard tool in preoperative planning.

关键词： Adolescent idiopathic scoliosis Optimal instrumentation configuration Interdisciplinary clinical problem particle swarm optimization algorithm

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