A global optimization technique for statistical classifier design
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毕业设计(论文)任务书题目:大功率双模微波滤波器的仿真设计系名信息工程系专业电子信息工程学号学生姓名指导教师种楠楠职称助教年月日一、原始依据(包括设计或论文的工作基础、研究条件、应用环境、工作目的等。
)工作基础:在全球无线通讯市场成长发展的趋势下,人们对各种无线通讯工具的要求越来越高,功率辐射小、作用距离远、覆盖范围大已成为各运营商乃至无线通讯设备制造商的普遍要求。
这就对无线通讯系统中的器件提出了更高的要求。
微波滤波器具有低插入损耗、边带陡峭度高、体积和重量小等优点,能够满足通信、航天航空、军事等领域的高速发展的需求,因此通过高效的设计方法开发出符合现代要求的GHz以上的微波滤波器具有十分重要的现实意义。
应用环境:本设计依据微波原理和电磁场与电磁波理论,设计一款能达到10W以上的L波段大功率滤波器。
学生完成对理论基础的熟悉和掌握后,通过在Genesys、ADS、Sonnet中仿真优化,设计符合要求的滤波器。
工作目的:本课题的主要任务是利用现有资源,设计仿真功率更高、选择性更好的滤波器。
二、参考文献[1] 张裕恒等.超导物理(第三版).合肥:中国科学技术大学出版社,2009[2] David M. Pozar[著],张肇仪,周乐柱,吴德明等[译].微波工程(第三版)[M]. 北京:电子工业出版社.2006.3[3] M. Nisenoff and W. J. Meyers, ‘On-orbit status of the high temperaturesuperconductivity space experiment’, IEEE Trans. Appl. Supercond., 2001, Vol. 11, No. 1, pp. 799-805.[4]M. Nisenoff, J. C. Ritter, G. Price, et al. ‘The high temperature superconductivityspace experiment: HTSSE I-components and HTSSE II subsystems and advanced devices’, IEEE Trans. Appl. Supercond., 1993, Vol. 3, pp. 2885-2890.[5] T. G. Kawecki, G. A. Golba, G. E. Price, V. S. Rose and W. J. Meyers, ‘The hightemperature superconductivity space experiment (HTSSE-II) design’, IEEE Trans.Microwave Theory Tech., 1996, Vol. 44, No. 7, pp. 1198-1212.[6] 黄席椿,高顺泉.滤波器综合法设计原理.北京:人民邮电出版社.1978.74[7] J.S. Hong,M.J. Lancaster.Microstrip Filters for RF/Microwave Applications.NewYork:Wiley,2001.384[8]Richard J. Cameron. ‘General Coupling Matrix Synthesis Methods for ChebyshevFiltering Fu nctions’. IEEE Trans. Microwave Theory Tech.,1999, Vol.17,pp.433-442 [9] Richard J. Cameron. ‘Advanced Coupling Matrix Synthesis Techniques for MicrowaveFilters’. IEEE Trans. Microwave Theory Tech.,2003, Vol.51,pp.1-10[10]R.Levy,’Direct synthesis of cascaded quadruplet(CQ) filter ‘, IEEE Trans.Microwave Theory Tech.,1995,vol.43,no.12,pp.2940-2945[11]H.C.Bell,’Canonical asymmetric coupled-resonator filters’, IEEE Trans.Microwave Theory Tech.,1982, vol.30,no.9,pp.1335-1340[12]Stefano Tamiazzo,Giuseppe M acchiarella.’An Analytical Technique for theSynthesis of Cascaded N-Tuplets Cross-Coupled Resonators Microwave Filters Using Matrix Rptations’ .IEEE Trans. Microwave Theory Tech.,2005,vol.53,no.5,pp.1693-1698.[13] W. A. Atia, K. A. Zaki, A. E. Atia. Synthesis of general topology multiple coupledresonator filters by optimization. IEEE Microwave Theory Tech. Dig,1998, 821~824 [14]A. B. Jayyousi, M. J. Lancaster. A Gradient-Based Optimization Technique EmployingDeterminants for the Synthesis of Microwave Coupled Filters. IEEE Microwave Theory Tech. Dig, 2004, 1369~1372[15]Smain Amari. Synthesis of Cross-coupled Resonator Filters Using an Analyticalradient Based Optimization Technique. IEEE Trans. Microwave Theory and Tech. ,2000 ,48 (9):1559~1564[16]左涛. ‘高温超导滤波器研究’.[博士学位论文]. 天津:南开大学,2008.[17]夏侯海.‘面向微波系统应用的高温超导器件研究’。
Global Optimization Toolbox 3.0Solve multiple maxima, multiple minima, and nonsmooth optimization problemsGlobal Optimization Toolbox provides methods that search for global solutions to problems that contain multiplemaxima or minima. It includes global search, multistart, pattern search, genetic algorithm, and simulatedannealing solvers. You can use these solvers to solve optimization problems where the objective or constraintfunction is continuous, discontinuous, stochastic, does not possess derivatives, or includes simulations orblack-box functions with undefined values for some parameter settings.Genetic algorithm and pattern search solvers support algorithmic customization. You can create a custom geneticalgorithm variant by modifying initial population and fitness scaling options or by defining parent selection,crossover, and mutation functions. You can customize pattern search by defining polling, searching, and otherfunctions.Key Features▪Interactive tools for defining and solving optimization problems and monitoring solution progress▪Global search and multistart solvers for finding single or multiple global optima▪Genetic algorithm solver that supports linear, nonlinear, and bound constraints▪Multiobjective genetic algorithm with Pareto-front identification, including linear and bound constraints▪Pattern search solver that supports linear, nonlinear, and bound constraints▪Simulated annealing tools that implement a random search method, with options for defining annealing process,temperature schedule, and acceptance criteria▪Parallel computing support in multistart, genetic algorithm, and pattern search solvers▪Custom data type support in genetic algorithm, multiobjective genetic algorithm, and simulated annealing solversnested inside multiple local minima (left), multiple local minima with no global minimum (right).Plot of a nonsmooth objective function (bottom) that is not easily solved using traditional gradient-based optimization techniques. The Optimization Tool (middle) shows the solution found using pattern search in Global Optimization Toolbox. Iterative results for function value and mesh size are shown in the top figure.Defining, Solving, and Assessing Optimization ProblemsGlobal Optimization Toolbox provides functions that you can access from the command line and from the Optimization Tool graphical user interface (GUI) in Optimization Toolbox™. Both the command line and GUI let you:▪Select a solver and define an optimization problem▪Set and inspect optimization options▪Run optimization problems and visualize intermediate and final results▪Use Optimization Toolbox solvers to refine genetic algorithm, simulated annealing, and pattern search results ▪Import and export optimization problems and results to your MATLAB® workspace▪Capture and reuse work performed in the GUI using MATLAB code generationYou can also customize the solvers by providing your own algorithm options and custom functions. Multistartand global search solvers are accessible only from the command line.Visualization of Rastrigin’s function (right) that contains many local minima and one global minimum (0,0). The genetic algorithm helps you determine the best solution for functions with several local minima, while the Optimization Tool (left) provides access to all key components for defining your problem, including the algorithm options.The toolbox includes a number of plotting functions for visualizing an optimization. These visualizations give you live feedback about optimization progress, enabling you to make decisions to modify some solver options or stop the solver. The toolbox provides custom plotting functions for both the genetic algorithm and pattern search algorithms. They include objective function value, constraint violation, score histogram, genealogy, mesh size, and function evaluations. You can show multiple plots together, open specific plots in a new window for closerexamination, or add your own plotting functions.Run-time visualizations (right) generated while the function is being optimized using genetic algorithm plot functions selected in the Optimization Tool (left).Using the output function, you can write results to files, create your own stopping criteria, and write your own application-specific GUIs to run toolbox solvers. When working from the Optimization Tool, you can export the problem and algorithm options to the MATLAB workspace, save your work and reuse it in the GUI at a later time,or generate MATLAB code that captures the work you’ve done.MATLAB file of an optimization created using the automatic code generation feature in the Optimization Tool. You can export an optimization from the GUI as commented code that can be called from the command line and used to automate routines and preserve your work.While an optimization is running, you can change some options to refine the solution and update performance results in genetic algorithm, multiobjective genetic algorithm, simulated annealing, and pattern search solvers. For example, you can enable or disable plot functions, output functions, and command-line iterative display during run time to view intermediate results and query solution progress, without the need to stop and restart the solver. You can also modify stopping conditions to refine the solution progression or reduce the number of iterations required to achieve a desired tolerance based upon run-time performance feedback.Global Search and Multistart SolversThe global search and multistart solvers use gradient-based methods to return local and global minima. Both solvers start a local solver (in Optimization Toolbox) from multiple starting points and store local and global solutions found during the search process.The global search solver:▪Uses a scatter-search algorithm to generate multiple starting points▪Filters nonpromising start points based upon objective and constraint function values and local minima already found▪Runs a constrained nonlinear optimization solver to search for a local minimum from the remaining start pointsThe multistart solver uses either uniformly distributed start points within predefined bounds or user-defined start points to find multiple local minima, including a single global minimum if one exists. The multistart solver runs the local solver from all starting points and can be run in serial or in parallel (using Parallel ComputingToolbox™). The multistart solver also provides flexibility in choosing different local nonlinear solvers. The available local solvers include unconstrained nonlinear, constrained nonlinear, nonlinear least-squares, and nonlinear least-squares curve fitting.Genetic Algorithm SolverThe genetic algorithm solves optimization problems by mimicking the principles of biological evolution, repeatedly modifying a population of individual points using rules modeled on gene combinations in biological reproduction. Due to its random nature, the genetic algorithm improves your chances of finding a global solution. It enables you to solve unconstrained, bound-constrained, and general optimization problems, and it does not require the functions to be differentiable or continuous.The following table shows the standard genetic algorithm options provided by Global Optimization Toolbox.Step Genetic Algorithm OptionCreation Uniform, feasibleFitness scaling Rank-based, proportional, top (truncation), shift linearSelection Roulette, stochastic uniform selection (SUS), tournament, uniform, remainder Crossover Arithmetic, heuristic, intermediate, scattered, single-point, two-pointMutation Adaptive feasible, Gaussian, uniformPlotting Best fitness, best individual, distance among individuals, diversity of population,expectation of individuals, max constraint, range, selection index, stoppingconditionsGlobal Optimization Toolbox also lets you specify:▪Population size▪Number of elite children▪Crossover fraction▪Migration among subpopulations (using ring topology)▪Bounds, linear, and nonlinear constraints for an optimization problemYou can customize these algorithm options by providing user-defined functions and represent the problem in a variety of data formats, for example by defining variables that are integers, mixed integers, categorical, or complex.You can base the stopping criteria for the algorithm on time, stalling, fitness limit, or number of generations. And you can vectorize your fitness function to improve execution speed or execute the objective and constraint functions in parallel (using Parallel Computing Toolbox).Output that shows solutions reached when using only the genetic algorithm (right, bar chart) and when using the genetic algorithm with a gradient-based solver from Optimization Toolbox (final point in Optimization Tool, left). Combining algorithms can produce more accurate results while reducing the number of function evaluations required by the genetic algorithm alone.Multiobjective Genetic Algorithm SolverMultiobjective optimization is concerned with the minimization of multiple objective functions that are subject to a set of constraints. The multiobjective genetic algorithm solver is used to solve multiobjective optimization problems by identifying the Pareto front—the set of evenly distributed nondominated optimal solutions. You can use this solver to solve smooth or nonsmooth optimization problems with or without bound and linear constraints. The multiobjective genetic algorithm does not require the functions to be differentiable or continuous.The following table shows the standard multiobjective genetic algorithm options provided by Global Optimization Toolbox.Step Multiobjective Genetic Algorithm OptionCreation Uniform, feasibleFitness scaling Rank-based, proportional, top (truncation), linear scaling, shiftSelection TournamentCrossover Arithmetic, heuristic, intermediate, scattered, single-point, two-pointMutation Adaptive feasible, Gaussian, uniformPlotting Average Pareto distance, average Pareto spread, distance among individuals,diversity of population, expectation of individuals, Pareto front, rank histogram,selection index, stopping conditionsGlobal Optimization Toolbox also lets you specify:▪Population size▪Crossover fraction▪Pareto fraction▪Distance measure across individuals▪Migration among subpopulations (using ring topology)▪Linear and bound constraints for an optimization problemYou can customize these algorithm options by providing user-defined functions and represent the problem in a variety of data formats, for example by defining variables that are integers, mixed integers, categorical, or complex.You can base the stopping criteria for the algorithm on time, fitness limit, or number of generations. And you can vectorize your fitness function to improve execution speed or execute the objective functions in parallel (using Parallel Computing Toolbox).Multiobjective genetic algorithm defined in the Optimization Tool (top), used to identify the Pareto front containing disconnected regions (middle) for the Kursawe function (bottom).Pattern Search SolverGlobal Optimization Toolbox contains three direct search algorithms: generalized pattern search (GPS), generating set search (GSS), and mesh adaptive search (MADS). While more traditional optimization algorithms use exact or approximate information about the gradient or higher derivatives to search for an optimal point, these algorithms use a pattern search method that implements a minimal and maximal positive basis pattern. The pattern search method handles optimization problems with nonlinear, linear, and bound constraints, and does not require functions to be differentiable or continuous.The following table shows the pattern search algorithm options provided by Global Optimization Toolbox. You can change any of the options from the command line or the Optimization Tool.Pattern Search Option DescriptionPolling methods Decide how to generate and evaluate the points in a pattern and the maximumnumber of points generated at each step. You can also control the polling orderof the points to improve efficiency.Search methods Choose an optional search step that may be more efficient than a poll step. Youcan perform a search in a pattern or in the entire search space. Global searchmethods, like the genetic algorithm, can be used to obtain a good starting point. Mesh Control how the pattern changes over iterations and adjusts the mesh forproblems that vary in scale across dimensions. You can choose the initial meshsize, mesh refining factor, or mesh contraction factor. The mesh acceleratorspeeds up convergence when it is near a minimum.Cache Store points evaluated during optimization of computationally expensiveobjective functions. You can specify the size and tolerance of the cache that thepattern search algorithm uses and vary the cache tolerance as the algorithmproceeds, improving optimization speed and efficiency.Nonlinear constraint algorithm settings Specify a penalty parameter for the nonlinear constraints as well as a penalty update factor.Using the Optimization Tool (top) to find the peak, or global optima, of the White Mountains (middle and bottom) using pattern search.Simulated Annealing SolverSimulated annealing solves optimization problems using a probabilistic search algorithm that mimics the physical process of annealing, in which a material is heated and then the temperature is slowly lowered to decrease defects, thus minimizing the system energy. By analogy, each iteration of a simulated annealing algorithm seeks to improve the current minimum by slowly reducing the extent of the search.The simulated annealing algorithm accepts all new points that lower the objective, but also, with a certain probability, points that raise the objective. By accepting points that raise the objective, the algorithm avoids being trapped in local minima in early iterations and is able to explore globally for better solutions.Simulated annealing allows you to solve unconstrained or bound-constrained optimization problems and does not require that the functions be differentiable or continuous. From the command line or Optimization Tool you can use toolbox functions to:▪Solve problems using adaptive simulated annealing, Boltzmann annealing, or fast annealing algorithms▪Create custom functions to define the annealing process, acceptance criteria, temperature schedule, plotting functions, simulation output, or custom data types▪Perform hybrid optimization by specifying another optimization method to run at defined intervals or atnormal solver terminationUsing simulated annealing to solve a challenging problem that contains flat regions between basins.Solving Optimization Problems Using Parallel ComputingYou can use Global Optimization Toolbox in conjunction with Parallel Computing Toolbox to solve problems that benefit from parallel computation. By using built-in parallel computing capabilities or defining a custom parallel computing implementation of your optimization problem, you decrease time to solution.Built-in support for parallel computing accelerates the objective and constraint function evaluation in genetic algorithm, multiobjective genetic algorithm, and pattern search solvers. You can accelerate the multistart solver by distributing the multiple local solver calls across multiple MATLAB workers or by enabling the parallel gradientestimation in the local solvers.Product Details, Demos, and System Requirements/products/global-optimizationTrial Software/trialrequestSales/contactsalesTechnical Support/support Demonstration of Using the Genetic Algorithm with Parallel Computing ToolboxA custom parallel computing implementation involves explicitly defining the optimization problem to use parallel computing functionality. You can define either your objective function or constraint function to use parallel computing, letting you decrease the time required to evaluate the objective or constraint.ResourcesOnline User Community /matlabcentral Training Services /training Third-Party Products and Services /connections Worldwide Contacts /contact© 2010 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See /trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.。
高速射频交换矩阵设计王胜海;王辉球【摘要】A design of high⁃speed RF switching matrix based on single⁃pole multiple⁃throw switch,used in communication system,is proposed in this paper. The design principle of RF matrix is analysed. The detailed design scheme of high⁃speed RF switching matrix working in L⁃band is given. The testing and detection results indicate that the design can satisfy the expected in⁃dex. In the integrated developing trend of electronic equipments,the design can provide super integrated function and ideas for miniaturization and integration of electronic equipments.%提出应用于通信系统中的基于单刀多掷开关(SPNT)的高速射频矩阵设计,分析射频矩阵设计原理,给出一个L波段的高速射频矩阵详细设计方案,通过实际测试检验,设计满足了预期指标。
在目前电子设备综合化发展的趋势下,可以提供良好的射频综合化功能,为电子设备的小型化综合化提供了解决思路。
【期刊名称】《现代电子技术》【年(卷),期】2015(000)007【总页数】4页(P69-72)【关键词】通信系统;单刀多掷开关;高速射频交换矩阵;设计方案【作者】王胜海;王辉球【作者单位】中国人民解放军92728部队,上海 200436;中国电子科技集团公司第二十研究所,陕西西安 710068【正文语种】中文【中图分类】TN710-34近几十年来,电子系统已经改变了传统的将不断出现的功能设备逐渐堆积式的向前发展模式,而是沿着网络化、综合化、模块化、通用化和智能化的方向发展。
The Model of the Water Content of the Dregs in Rotary Dryer Kiln Based on SVMXin Wang 1,2, Chunhua Yang 2, Bin Qin 11. Department of Electrical Engineering, Hunan University of Technology, Hunan Zhuzhou, 412008, China2. School of Information Science and Engineering, Central South University, Hunan Changsha 410083,Chinaemail: Abstract - Based on analysis of the process of rotary dryer kiln, a soft- sensor modelfor water content of the dregs by using the support vector machines (SVM) is proposed. The parameters of SVM are optimized through the hybrid optimization algorithm which combines the genetic search with the local search, first the kernel function and SVM parameters are optimized roughly through genetic algorithm, after certain generations, the kernel parameter is fine adjusted by local linear search. Experiments of acquiring the sample data are designed and the soft-sensor model has been obtained and used successfully in the inference control of rotary dryer kiln. The proposed method can not only overcome the difficulty in determining the structure and parameters of using other models such as RBF model but also has better generalization performance than other models.Index Terms-rotary dryer kiln; water content of the dregs;soft-sensor model; support vector machines regression;parameter selection; hybrid optimizationI . INTRODUCTIONThe process of rotary dryer kiln is an important procedure for the hydrometallurgy ofzinc, and the water content of dregs is the key production index for the drying process. Red-ray light measurement and neutron moisture meters measurement are the main methods in actual production process, however, the former has not been completely matured and the latter is rarely used due to radiation and high price. As a result, there is no online measurement for most production process and the water content of dregs has to be controlled indirectly through workers' experience which is hard to meet the control requirement. So it is very important to realize online measurement for the water content of dregs through soft-sensor technology.The design of soft-sensor is to select a group of secondvariables, which are tightly related with the main variableand can be easily measured, and to realize the online estimation of main variable by establishing some mathematic model between them [1]. The ARMAX and RBF Neural network model have been used successfully in the soft-sensor modeling of water content [2], but the ARMAX model is a linearized model about a certain work point and large prediction errors will be produced if theactual work point departs from the original work point, the RBF network model is difficult to determine its structure and solve the over-fitting problem for the training data set. Support vector machines introduced by Vapnik [3] are machine learning methods based on statistic theory, which can solve above problems. In the present work a prediction model based on SVM has been developed, a new hybrid optimization method for parameter selection of SVM is proposed. The soft-sensor model is obtained through the sample date set collect from experiments.II. FLOWSHEET OF ROTARY DRYER KILNThe flowsheet of rotary dryer kiln is shown as figure 1, which consists of kiln head, dryer drum, kiln trail and append devices.The dregs with 35-40 % water content are fed by the circle feeder, the exhaust gas is produced by burning gas in the head of kiln and the water content of dregs is decreased to 15-17% in the dryer drum through heat exchange between exhaust gas and dregs which is mainly achieved by convection, then the dregs are transported to the next procedure. The task of the rotary dryer kiln control consists of three parts: combustioncontrol, water content control and sequence logic control. The soft-sensor of the water content is the key factor for thecontrol system.III. MODEL FOR THE WATER CONTENT OF ROTARY DRYERKILN DREGSA. Support vector regression (SVR)SVMs can be applied to regression problems by the introduction of a loss function [3].Suppose there is a given set of samples SVM regression maps the data into a higher dimensional feature space via a nonlinear mapping ф , and then regresses linearly in this feature space. An optimal decision function can beformulated as (1).(1) Where the vector n W R ∈ and bias b R ∈ . So the nonlinear estimation function istranslated to a linear estimation function in a higher dimensional feature space. By applying the minimization rule of the structural and empirical risks, for a linearε-insensitive loss function, introduce the positive slack variablesi ξ,i ξ*and the task is therefore changed into:(2) Where 2||||w is the complexity of the controlling model,C is the regularization constant determining the trade-offbetween the empirical risk and the structural risk.With constraints:After kernel substitution the dual objective function is:(3)On the conditions that:AndWhere i α*,i α are the introduced Lagrange multipliers, and K(,i j x x ) is the kernelfunction which satisfies the Mercer condition. Choose different type of kernel functions and different SVMs can be constructed. In this research we use the Gaussian kernelThe quadratic programming problem (3) can be solved through sequential minimal optimization (SMO) [4] and derivatives decomposition methods [5]. Only some coefficients (i α*-i α ) will be nonzero, and the data points associated with them refer tosupport vectors (SVs). Given the number of support vector m, the function modeling data is then:(4)The bias, b, can be calculated by considering Kurash-Kuhn-Tucker (KIKT) conditions for regression.B. Structure of the model and selection of the second variablesThe structure of the prediction model based on SVM is shown as Fig. 2, it has three layers, a Gaussian kernel is used in the middle layer and the nodes in middle layers are formed by SVR automatically. The output represents the prediction value of the water content of rotary kiln dregs.Selecting the input variables from all possible input variables is important for system modeling. Based on the model of technology, experiments and analysis of the relativity between the second variables and the main variable, we have selected the fuel flow mass f Q , difference of temperatures between kiln head and trail aT ∆ temperature in the middle of drum Tm, the speed of drum V etc as the second variables, and the water content of rotary kiln dregs Ms as the output variable.C . Hybrid optimization of SVM ParametersIt is well known that SVM generalization performance (estimation accuracy) depends on a good setting of parameters such as regularization constant C, insensitive coefficient ε and the kernel parameters. Generally the empirical error will decrease monotono usly with C and come to remain constant when C is big enough. The generalization error on the test will first decrease immonotonously with C, come to an almost constant value as C changes in a certain zone, and then increase when C goes beyond a certain value. Training time will increase when C increases. Parameter ε controls the width of theε-insensitive zone used to fit the training data. The value of ε can affect the number of SVMs used to construct the regression function. Larger ε-value results in fewerselected SVMs, less complex regression estimation and less training time. Kernel function type and parameter (σ and d) that implicitly defines the nonlinear mapping from input space to some higher dimensional feature space are also very important to the performance of SVM. A large amount of experiments have demonstrated that the width parameter σ in the Gaussian kernel function strongly affects generalization performance of SVM [6]. It is well-known that the value of ε should be proportional to the input noise level, that is ε ∝V . Experiments show that the RMSE on the training set increasest(1)with σ. On the other hand, the RMSE on the test set decreases initially but increases subsequently as σ increases. This indicates that too small avalue causes SVM an over-fit while too large a value causesan under-fit of the training data. An appropriate value for σcan only be obtained in a certain zone [7].In this paper a hybrid optimization algorithm for SVMparameter selection is proposed, first an evolutionary SVMis used to search the kernel function and its trainingparameters with the training sample set, and after certaingenerations the kernel parameter is fine adjusted by localsearch. The tentative SVMs are tested by the validation sample set. The training process of the SVM will be completed when the identified SVM can give good generalized predictions for validation samples. The algorithm combines the ability of GA which widely sample a search space with the accelerating search ability of local search. To find the global optimum the hybrid optimization algorithm involves five main parts.1) Performance criterionCross-validation is a popular technique for estimatinggeneralization error and there are several versions. In k-foldcross-validation, the training data is randomly split into k mutually exclusive subsets (the folds) of approximately equal size. In this study, in order to simplify the algorithm,we divide the sample data set into three folds, one for training, one for validation (calculating the performance fitness of the model) and one for testing.2) Chromosome representationBased on analysis above, the constant C and ε have less influence on the generalization error than kernel parameter, and the kernel parameter will be fine adjusted later, so a binary code form is adopted to decrease the computation cost. A direct code is used to code the parameter ε, a logarithm mapping is used before coding the value of C and the Gaussian kernel width σ. The range of the parameters can be estimated through the methods proposed in [6] or prior knowledge of the system.3) Fitness calculationThe root mean square error obtained by validation data set is used to calculate the performance:(5)This performance index may be changed with the actual problem. The fitness is calculated by:(6)Where I is an individual in population,E is the maximum root mean square error inmaxpopulation and E(I) is the root mean square error of individual.4) Genetic operationIn this paper, two point crossover and non-uniform-mutation function and a normalizedp for each geometric ranking method [8] are used. In this method, the probabilityiselected individual is given as(7)Where q represents the probability of selecting the best individual, r is the rank of the individual (with 1 being thebest), andN denotes the population size.p5) Local research for kernel parameterWhen the parameters are set in the appropriate zone by using the GA, we can use a local search to fine adjust the kernel function parameter, in this paper we mainly focus on the Gaussian kernel and adopt the approach of literature [9]. The selection of best σ value is according to (8).(8)Whereσ-is the previous value, iσis the current value.1iThe hybrid optimization algorithm can be summarized as follows:Step1): Given a sample set, a testing sample set and a validation data set are constructed by selecting randomly from the sample set, the rest sample setform the training set. The range of parameters using GA is determined by theestimation method mentioned in above section.Step2): Initialize evolution parameters such as populationsize, the number of evolutionary generations, interval generations of localsearch (T), parameters of SVM including ε, C and kernel width σ, initializethe population chromosome randomly.Step3): Decode the chromosome of population, produce a set of C, ε andkernel parameter σ in the given ranges according to the decoding result. Usethe SMO algorithm to solve the quadratic programming problems, use theKKT condition to obtain the bias b. then to obtain their support vectorsmachines model.Step4): Use the validation samples to calculate the prediction error of theSVM models. The applicability of the model is indicated by fitness.Step5): After every T generations select a few higher fitness solutions toperform a local search.Step6): If performance is accepted or the maximal generations is achievedthen the training procedure of the SVM is completed, go to step 7. Otherwise,perform a crossover operation and mutation to create individuals of newgenerations, then go to step 3.Step7): The model with minimal validation error is expected. Use the testsamples to calculate the generalization error.IV. EXPERIMENT PROCEDURE AND DATA PREPROCESSA. Experiment procedureBecause the instrument for measuring the water content can not work properly, we utilize the heating and weighting measurement method to obtain the data of water content. In Order to cover the normal fluctuation in all the measured variables related with the output, several experimental tests were performed. The plant was excited inQ order to be able to determine dynamic models for the soft-sensor. The set points off controllers were varied in a pseudo-random binary manner, as shown in Figure. 3(b). The fluctuations of some other measured variables are shown in figure. 3(a).B. Data preprocess and resultsThe experiments data are used to train the model, 185 sample data are collected and divided into three folds. Considering the numerical influence and the error, the data must be preprocessed which contains normalizing and error rectification. The error can be divided into random errorand gross error, for the random error the move mean digitalfilter and data reconciliation are used, for the theFig. 3 Measurements in a persistent excitation testgross error detection [10] is used to eliminate its influence. In the proposed hybrid optimization algorithm, following parameter specifications are used: Population size is set at 30, Number of generation is 25, Number of neighbors of cT to be examined is 15 and T is chosen as 5. Crossover probability is set at 0.8 and Mutation probability is 0.25.We can ascertain the importance of each input variable from this initial model. The basic idea is simply to remove all antecedent clauses except one associated with a particular input variable from the rules and then to compute the fuzzyy outpput withresppect to this inpput variable. The larger the output change caused by a specified input variable, the more important this input variable is. After the rank validating the input variables ()f t Q and (1)t V with less influence is deleted. Then the model wasretrained using proposed approach and other methods.Table 1 shows the results comparing SVR-based model,ARMAX model and RBF model, table 2 shows the results comparing proposedapproach in this paper and other methods of choosing hyper-parameters of SVM, where MAE is maximum absolute error. Fig.4 shows the prediction value and the actual value of the model. The SVR based on the Gaussian kernel can be regarded as a special RBF network, with a good structure and staticparameters which has a minimal structure risk and higher prediction accuracy comparing to the ordinary RBF network. The assembled optimal parameters for SVR can be found in the proposed hybrid optimization algorithm.TABLE IPREDICTION RESULTS COMPARING OF ARMAX, RBF NETWORK AND SVMV. CONCLUSIONS1) The prediction model of water content of dregs in rotary dryer kiln using SVR ispresented. It can overcome the difficulty in determining the structure and parameters of using other models such as RBF model.2) More dynamic information can be excited by the designed experiments foracquiring the sample data and so the performance of model can be improved.3) Comparisons of application results suggest that the proposed parameter selectionyield better generalization performance of SVM estimation and the prediction model of water content of dregs in rotary dryer kiln can obtain relative high performance.ACKNOWLEDGMENTThe work is supported by the National Natural Science Foundation of China (No 60574030) and by Foundation of Educational Department of Hunan Province (No05C523).REFERENCES[1] M T Tham, A J Morris, G A Montague. "Soft-sensors for processestimation and inferential control," J Proc Cont, vol 1. pp. 3-14,jan.,1991.[2] Bin Qin, Xin Wang, Min Wu et al. "The Soft-sensor for the Water Content ofthe Dregs in Rotary Dryer Kiln Based on Hybrid Chaos Optimization Algorithm. Control theory and application," vol 22. pp. 825-828, Oct. 2005.[3] V. Vapnik, The nature of statistical learning theory. New York: Springer, 1995[4] G.W.Flake, Lawrence S, "Efficient SVM regression training with SMO,"Machine Learning, vol 46. pp. 271-290, Jan.-Mar.,2002.[5] skov, "Feasible direction decomposition algorithms for training supportvector machines," Machine Learning, vol 46. pp. 315-350, Jan.-Mar. 2002. [6] Vladimir Cherkassky, Yunqian Ma "Practical selection of SVMparameters and noise estimation for SVM regression," Neural Networks, vol 17. pp. 113-126, Jan. 2004.[7] Wenjian Wang, Zongben Xua , Weizhen Luc et al. "Determination of the spread parameter in the Gaussian kernel for classification and regression, " Neurocomputing, vol 55. pp. 643- 663, Jun. 2003.[8] Z. Michalewicz, Genetic algorithms+Data Structures=Evolution Programs, 3rd Edition, NewYork: Springer, USA, 1999.[9] O.Chapelle, V.Vapnik, O.Bousquet, et al. "Choosing multipleparameters for support vector machines". Machine Learning, vol 46.pp. 59-13 1, Jan.-Mar. 2002.[10] Hongjun Li, Yongsheng Qin, Yongmao Xu. "Data Reconcilliation andGross Error Detection in Chemical Process." Control and Instruments in Chemical Industry, vol 24, pp. 25-32, Feb. 1997.。
A dynamic replica management strategy in data grid--- Journal of Network and Computer Applications expired, propose, indicate, profitable, boost, claim, present, congestion, deficiency, moderately, metric, turnaround, assume,specify, display, illustrate, issue,outperform over .... about 37%, outperform ....lead todraw one's attentionaccordinglyhave great influence ontake into accountin terms ofplay major role inin comparison with, in comparison toi.e.=(拉丁)id estReplication is a technique used in data grid to improve fault tolerance and to reduce the bandwidth consumption.Managing this huge amount of data in a centralized way is ineffective due to extensive access latency and load on the central server.Data Grids aggregate a collection of distributed resources placed in different parts of the world to enable users to share data and resources.Data replication is an important technique to manage large data in a distributed manner.There are three key issues in all the data replication algorithms which are replica placement, replica management and replica selection.Meanwhile, even though the memory and storage size of new computers are ever increasing, they are still not keeping up with the request of storing large number of data.each node along its path to the requester.Enhanced Dynamic Hierarchical Replication and Weighted SchedulingStrategy in Data Grid--- Journal of Parallel and Distributed Computing duration, manually, appropriate, critical, therefore, hybrid, essential, respectively, candidate, typically, advantage, significantly, thereby, adopt, demonstrate, superiority, scenario, empirically, feasibility, duplicate, insufficient, interpret, beneficial, obviously, whilst, idle, considerably, notably, consequently, apparently,in a wise manneraccording tofrom a size point of viewdepend oncarry outis comprised ofalong withas well asto the best of our knowledgeBest replica placement plays an important role for obtaining maximum benefit from replication as well as reducing storage cost and mean job execution time.Data replication is a key optimization technique for reducing access latency and managing large data by storing data in a wise manner.Effective scheduling in the Grid can reduce the amount of data transferred among nodes by submitting a job to a node where most of the requested data files are available.Effective scheduling of jobs is necessary in such a system to use available resources such as computational, storage and network efficiently.Storing replicas close to the users or grid computation nodes improves response time, fault tolerance and decreases bandwidth consumption.The files of Grid environments that can be changed by Grid users might bring an important problem of maintaining data consistency among the various replicas distributed in different machines.So the sum of them along with the proper weight (w1,w2) for each factor yields the combined cost (CCi,j) of executing job i in site j.A classification of file placement and replication methods on grids--- Future Generation Computer Systems encounter, slightly, simplistic, clairvoyance, deploy, stringent, concerning, properly, appropriately, overhead, motivate, substantial, constantly, monitor, highlight, distinguish, omit, salient, entirely, criteria, conduct, preferably, alleviate, error-prone, conversely,for instanceaccount forhave serious impact ona.k.a.= also known asconsist inaim atin the hands offor .... purposesw.r.t.=with regard toconcentrate onfor the sake ofbe out of the scope of ...striping files in blocksProduction approaches are slightly different than works evaluated in simulation or in controlled conditions....File replication is a common solution to improve the reliability and performance of data transfers.Many file management strategies were proposed but none was adopted in large-scale production infrastructures.Clairvoyant models assume that resource characteristics of interest are entirely known to the file placement algorithm.Cooperation between data placement and job scheduling can improve the overall transfer time and have a significant impact on the application makespan as shown in.We conclude that replication policies should rely on a-priori information about file accesses, such as file type or workflow relation.Dynamic replica placement and selection strategies in data grids----Acomprehensive survey--- Journal of Parallel and Distributed Computing merit, demerit, tedious, namely, whereas, various, literature, facilitate, suitable, comparative, optimum, retrieve, rapid, evacuate, invoke, identical, prohibitive, drawback, periodically,with respect toin particularin generalas the name indicatesfar apartconsist of , consist inData replication techniques are used in data grid to reduce makespan, storage consumption, access latency and network bandwidth.Data replication enhances data availability and thereby increases the system reliability.Managing dynamic architecture of the grid, decision making of replica placement, storage space, cost of replication and selection are some of the issues that impact the performance of the grid.Benefits of data replication strategies include availability, reliability, scalability, adaptability and improved performance.As the name indicates, in dynamic grid, nodes can join and leave the grid anytime.Any replica placement and selection strategy tries to improve one or more of the following parameters: makespan, quality assurance, file missing rate, byte missing rate, communication cost, response time, bandwidth consumption, access latency, load balancing, maintenance cost, job execution time, fault tolerance and strategic replica placement.Identifying Dynamic Replication Strategies for a High-PerformanceData Grid--- Grid Computing 2001 identify, comparative, alternative, preliminary, envision, hierarchical, tier, above-mentioned, interpret, exhibit, defer, methodology, pending, scale, solely, churn outlarge amounts ofpose new problemsdenoted asadapt toconcentrate on doingconduct experimentssend it offin the order of petabytesas of nowDynamic replication can be used to reduce bandwidth consumption and access latency in high performance “data grids” where users require remote access to large files.A data grid connects a collection of geographically distributed computer and storage resources that may be located in different parts of a country or even in different countries, and enables users to share data and other resources.The main aims of using replication are to reduce access latency and bandwidth consumption. Replication can also help in load balancing and can improve reliability by creating multiple copies of the same data.Group-Based Management of Distributed File Caches--- Distributed Computing Systems, 2002 mechanism, exploit, inherent, detrimental, preempt, incur, mask, fetch, likelihood, overlapping, subtle,in spite ofcontend withfar enough in advancetake sth for granted(be) superior toDynamic file grouping is an effective mechanism for exploiting the predictability of file access patterns and improving the caching performance of distributed file systems.With our grouping mechanism we establish relationships by observing file access behavior, without relying on inference from file location or content.We group files to reduce access latency. By fetching groups of files, instead of individual files, we increase cache hit rates when groups contain files that are likely to be accessed together.Further experimentation against the same workloads demonstrated that recency was a better estimator of per-file succession likelihood than frequency counts.Job scheduling and data replication on data grids--- Future Generation Computer Systems throttle, hierarchical, authorized, indicate, dispatch, assign, exhaustive, revenue, aggregate, trade-off, mechanism, kaleidoscopic, approximately, plentiful, inexact, anticipated, mimic, depict, exhaust, demonstrate, superiority, namely, consume,to address this problemdata resides on the nodesa variety ofaim toin contrast tofor the sake ofby means ofplay an important role inhave no distinction betweenin terms ofon the contrarywith respect toand so forthby virtue ofreferring back toA cluster represents an organization unit which is a group of sites that are geographically close.Network bandwidth between sites within a cluster will be larger than across clusters.Scheduling jobs to suitable grid sites is necessary because data movement between different grid sites is time consuming.If a job is scheduled to a site where the required data are present, the job can process data in this site without any transmission delay for getting data from a remote site.RADPA: Reliability-aware Data Placement Algorithm for large-scale network storage systems--- High Performance Computing and Communications, 2009 ever-going, oblivious, exponentially, confront,as a consequencethat is to saysubject to the constraintit doesn't make sense to doMost of the replica data placement algorithms concern about the following two objectives, fairness and adaptability.In large-scale network storage systems, the reliabilities of devices are different relevant to device manufacturers and types.It can fairly distributed data among devices and reorganize near-minimum amount of data to preserve the balanced distribution with the changes of devices.Partitioning Functions for Stateful Data Parallelism in Stream Processing--- The VLDB Journal skewed, desirable, associated, exhibit, superior, accordingly, necessitate, prominent, tractable, exploit, effectively, efficiently, transparent, elastically, amenable, conflicting, concretely, exemplify, depict,a deluge ofin the form of continuous streamslarge volumes ofnecessitate doingas a examplefor instancein this scenarioAccordingly, there is an increasing need to gather and analyze data streams in near real-time to extract insights and detect emerging patterns and outliers.The increased affordability of distributed and parallel computing, thanks to advances in cloud computing and multi-core chip design, has made this problem tractable.However, in the presence of skew in the distribution of the partitioning key, the balance properties cannot be maintained by the consistent hash.MORM: A Multi-objective Optimized Replication Management strategyfor cloud storage cluster--- Journal of Systems Architecture issue, achieve, latency, entail, consumption, article, propose, candidate, conclusively, demonstrate, outperform, nowadays, huge, currently, crucial, significantly, adopt, observe, collectively, previously, holistic, thus, tradeoff, primary, therefore, aforementioned, capture, layout, remainder, formulate, present, enormous, drawback, infrastructure, chunk, nonetheless, moreover, duration, substantially, wherein, overall, collision, shortcoming, affect, further, address, motivate, explicitly, suppose, assume, entire, invariably, compromise, inherently, pursue, handle, denote, utilize, constraint, accordingly, infeasible, violate, respectively, guarantee, satisfaction, indicate, hence, worst-case, synthetic, assess, rarely, throughout, diversity, preference, illustrate, imply, additionally, is an important issuea series ofin terms ofin a distributed mannerin order toby defaultbe referred to astake a holistic view ofconflict witha variety ofis highly in demandgiven the aforementioned issue and trendtake into accountyield close toas followstake into considerationwith respect toa research hot spotcall foraccording todepend upon/onmeet ... requirementfocus onis sensitive tois composed ofconsist offrom the latency minimization perspectivea certain number ofis defined as (follows) / can be expressed as (follows) /can be calculated/computed by / is given by the followingat handcorresponding tohas nothing to do within addition toas depicted in Fig.1et al.The volume of data is measured in terabytes and some time in petabytes in many fields.Data replication allows speeding up data access, reducing access latency and increasing data availability.How many suitable replicas of each data should be created in the cloud to meet a reasonable system requirement is an important issue for further research.Where should these replicas be placed to meet the system task fast execution rate and load balancing requirements is another important issue to be thoroughly investigated.As the system maintenance cost will significantly increase with the number of replicas increasing, keeping too many or fixed replicas are not a good choice.Where should these replicas be placed to meet the system task fast execution rate and load balancing requirements is another important issue to be thoroughly investigated.We build up five objectives for optimization which provides us with the advantage that we can search for solutions that yield close to optimal values for these objectives.The shortcoming of them is that they only consider a restricted set of parameters affecting the replication decision. Further, they only focus on the improvement of the system performance and they do not address the energy efficiency issue in data centers.Data node load variance is the standard deviation of data node load of all data nodes in the cloud storage cluster which can be used to represent the degree of load balancing of the system.The advantage of using simulation is that we can easily vary parameters to understand their individual impact on system performance.Throughout the simulation, we assumed "write-once, read-many" data and did not include the consistency or write and update propagations costs in the study.Distributed replica placement algorithms for correlated data--- The Journal of Supercomputing yield, potential, congestion, prolonged, malicious, overhead, conventional, present, propose, numerous, tackle, pervasive, valid, utilize,develop a .... algorithmsuffer fromin a distributed mannerbe denoted as Mconverge toso on and so forthWith the advances in Internet technologies, applications are all moving toward serving widely distributed users.Replication techniques have been commonly used to minimize the communication latency by bringing the data close to the clients and improve data availability.Thus, data needs to be carefully placed to avoid unnecessary overhead.These correlations have significant impact on data access patterns.For structured data, data correlated due to the structural relations may be frequently accessed together.Assume that data objects can be clustered into different classes due to user accesses, and whenever a client issues an access request, it will only access data in a single class.One challenge for using centralized replica placement algorithms in a widely distributed system is that a server site has to know the (logical) network topology and the resident set of all structured data sets to make replication decisions.We assume that the data objects accessed by most of the transactions follow certain patterns, which will be stable for some time periods.Locality-aware allocation of multi-dimensional correlated files on thecloud platform--- Distributed and Parallel Databases enormous, retrieve, prevailing, commonly, correlated, booming, massive, exploit, crucial, fundamental, heuristic, deterministic, duplication, compromised, brute-force, sacrifice, sophisticated, investigate, abundant, notation, as a matter of factin various wayswith .... taken into considerationplay a vital role init turns out thatin terms ofvice versaa.k.a.= also known asThe effective management of enormous data volumes on the Cloud platform has attracted devoting research efforts.Currently, most prevailing Cloud file systems allocate data following the principles of fault tolerance and availability, while inter-file correlations, i.e. files correlated with each other, are often neglected.There is a trade-off between data locality and the scale of job parallelism.Although distributing data randomly is expected to achieve the best parallelism, however, such a method may lead to degraded user experiences for introducing extra costs on large volume of remote accesses, especially for many applications that are featured with data locality, e.g., context-aware search, subspace oriented aggregation queries, and etc.However, there must be several application-dependent hot subspaces, under which files are frequently being processed.The problem is how to find a compromised partition solution to well serve the file correlations of different feature subspaces as much as possible.If too many files are grouped together, the imbalance cost would raise and degrade the scale of job parallelism;if files are partitioned into too many small groups, data copying traffic across storage nodes would increase.Instead, our solution is to start from a sub-optimal solution and employ some heuristics to derive a near optimal partition with as less cost as possible.By allocating correlated files together, significant I/O savings can be achieved on reducing the huge cost of random data access over the entire distributed storage network.Big Data Analytics framework for Peer-to-Peer Botnet detection usingRandom Forests--- Information Sciences magnitude, accommodate, upsurge, issue, hence, propose, devise, thereby, has struggled toit was revealed thatis expanding exponentiallytake advantage ofin the pastin the realm ofover the last few yearsthere has also been research onin a scalable manneras per the current knowledge of the authorson the contraryin naturereport their work onNetwork traffic monitoring and analysis-related research has struggled to scale for massive amounts of data in real time.In this paper the authors build up on the progress of open source tools like Hadoop, Hive and Mahout to provide a scalable implementation of quasi-real-time intrusion detection system.As per the current knowledge of the authors, the area of network security analytics severely lacks prior research in addressing the issue of Big Data.Improving pattern recognition accuracy of partial discharges by newdata preprocessing methods--- Electric Power Systems Research stochastic, oscillation, literature, utilize, conventional, derive, distinctive, discriminative, artificial, significantly, considerably, furthermore, likewise, Additionally, reasonable, symbolize, eventually, scenario, consequently, appropriate, momentous, conduct, depict, waveshape, deficiency, nonetheless, derived, respectively, suffer from, notably,be taken into considerationby means ofto our best knowledgein accordance withwith respect toas mentionedwith regard tobe equal withlead tofor instancein additionin comparison toThus, analyzing the huge amount of data is not feasible unless data pre-processing is manipulated.As mentioned, PD is completely a random and nonlinear phenomenon. Since ANNs are the best classifiers to model such nonlinear systems, PD patterns can be recognized suitably by ANNs.In other words, when classifier is trained after initial sophistications based on the PRPD patterns extracted from some objects including artificial defects, it can be efficiently used in practical fields to identify the exactly same PD sources by new test data without any iterative process.In pulse shape characterization, some signal processing methods such as Wavelet or Fourier transforms are usually used to extract some features from PD waveshape. These methods are affected by noise and so it is necessary to incorporate some de-noising methods into the pattern recognition process.PD identification is usually performed using PRPD recognition which is not influenced by changing the experimental set up.Partial Discharge Pattern Recognition of Cast Resin CurrentTransformers Using Radial Basis Function Neural Network--- Journal of Electrical Engineering & Technology propose, novel, vital, demonstrate, conduct, significant,This paper proposes a novel pattern recognition approach based on the radial basis function (RBF) neural network for identifying insulation defects of high-voltage electrical apparatus arising from partial discharge (PD).PD measurement and pattern recognition are important tools for improving the reliability of the high-voltage insulation system.。
Machine Learning OptimizationTechniquesare essential for improving the performance and efficiency of machine learning models. In this article, we will explore some of the most commonly used optimization techniques in machine learning.One of the most popular optimization techniques used in machine learning is gradient descent. Gradient descent is an iterative optimization algorithm that aims to find the minimum of a function by updating the parameters in the direction of the negative gradient of the function. This allows the model to converge to the optimal solution over multiple iterations.Another widely used optimization technique is stochastic gradient descent (SGD). In SGD, instead of calculating the gradient of the entire dataset, a random subset of the data is used to update the parameters of the model. This can speed up the optimization process, especially for large datasets, as it reduces the computational complexity of computing the gradients.Adam optimization is another popular optimization technique that combines the advantages of both gradient descent and stochastic gradient descent. Adam uses adaptive learning rates for each parameter, which allows the model to converge faster and more efficiently compared to traditional optimization algorithms.In addition to these optimization techniques, regularization methods such as L1 and L2 regularization are commonly used to prevent overfitting in machine learning models. Regularization adds a penalty term to the loss function, which helps in controlling the complexity of the model and reduces the likelihood of overfitting.Furthermore, batch normalization is another optimization technique that is used to improve the training of deep neural networks. Batch normalization normalizes the inputsof each layer in the neural network, which helps in stabilizing and accelerating the training process.Another important optimization technique is dropout, which is used to prevent overfitting in neural networks. Dropout randomly selects a subset of neurons to be ignored during training, which helps in improving the generalization ability of the model.In conclusion, machine learning optimization techniques play a crucial role in improving the performance and efficiency of machine learning models. By incorporating techniques such as gradient descent, stochastic gradient descent, Adam optimization, regularization, batch normalization, and dropout, machine learning models can achieve better accuracy and generalization on various tasks. It is essential for machine learning practitioners to understand and implement these optimization techniques effectively to build robust and accurate machine learning models.。
遗传算法多项式变异英语Genetic Algorithm Polynomial MutationGenetic algorithms (GAs) are a powerful optimization technique inspired by the principles of natural selection and evolution. They are particularly well-suited for solving complex, non-linear problems where traditional optimization methods may struggle. One crucial component of genetic algorithms is the mutation operator, which introduces random changes to the individuals in the population, helping to explore new regions of the search space and prevent premature convergence.Polynomial mutation is a specific type of mutation operator that has gained popularity in the field of genetic algorithms. This mutation scheme is designed to provide a more controlled and adaptive approach to the exploration of the search space, offering several advantages over simpler mutation operators.The key idea behind polynomial mutation is to use a polynomial function to control the probability distribution of the mutation step size. This allows for a more gradual and fine-tuned exploration of the search space, as opposed to the more abrupt and potentiallydisruptive changes introduced by other mutation operators.The mathematical formulation of polynomial mutation is as follows. Let x be the individual (solution) to be mutated, and let x_new be the mutated individual. The mutation process is defined as:x_new = x + delta * (x_ub - x_lb)where:- x_ub and x_lb are the upper and lower bounds of the search space, respectively.- delta is the mutation step size, which is determined by a polynomial probability distribution function.The polynomial probability distribution function is given by:delta = rand * (2 * rand)^(1/(eta + 1)) - 1where:- rand is a random number between 0 and 1.- eta is a parameter that controls the shape of the probability distribution.The parameter eta is crucial in determining the behavior of the polynomial mutation operator. A larger value of eta results in a morelocalized search, with smaller mutation steps being more likely. Conversely, a smaller value of eta leads to a more exploratory search, with larger mutation steps being more probable.One of the key advantages of polynomial mutation is its ability to adapt the mutation step size based on the progress of the optimization process. Early in the search, when the population is still exploring the search space, larger mutation steps are more beneficial to help discover promising regions. As the search progresses and the population converges towards the optimal solution, smaller mutation steps become more appropriate to fine-tune the search and avoid disrupting the progress made so far.Another benefit of polynomial mutation is its ability to handle constraints and boundaries more effectively. By scaling the mutation step size based on the distance to the boundaries, polynomial mutation can ensure that the mutated individuals remain within the feasible search space, avoiding the need for additional repair mechanisms.Furthermore, polynomial mutation has been shown to perform well across a wide range of optimization problems, including both continuous and combinatorial problems. Its versatility and adaptability make it a popular choice among genetic algorithm practitioners and researchers.In conclusion, polynomial mutation is a powerful and versatile mutation operator that has become an integral part of many successful genetic algorithm implementations. Its ability to adaptively control the mutation step size, handle constraints, and explore the search space effectively makes it a valuable tool in the optimization toolbox. As the field of genetic algorithms continues to evolve, the study and refinement of polynomial mutation and other mutation operators will undoubtedly remain an active area of research and development.。
粒子群优化算法论文粒子群优化算法摘要近年来,智能优化算法—粒子群算法(particle swarm optimization,简称PSO)越来越受到学者的关注。
粒子群算法是美国社会心理学家JamesKennedy 和电气工程师Russell Eberhart在1995年共同提出的,它是受到鸟群社会行为的启发并利用了生物学家Frank Heppner的生物群体模型而提出的。
它用无质量无体积的粒子作为个体,并为每个粒子规定简单的社会行为规则,通过种群间个体协作来实现对问题最优解的搜索。
由于算法收敛速度快,设置参数少,容易实现,能有效地解决复杂优化问题,在函数优化、神经网络训练、图解处理、模式识别以及一些工程领域都得到了广泛的应用。
PSO是首先由基于不受约束的最小化问题所提出的基于最优化技术。
在一个PSO系统中,多元化解决方案共存且立即返回。
每种方案被称作“微粒”,寻找空间的问题的微粒运动着寻找目标位置。
一个微粒,在他寻找的时间里面,根据他自己的以及周围微粒的经验来调整他的位置。
追踪记忆最佳位置,遇到构建微粒的经验。
因为那个原因,PSO占有一个存储单元(例如,每个微粒记得在过去到达时的最佳位置)。
PSO系统通过全局搜索方法(通过)搜索局部搜索方法(经过自身的经验),试图平衡探索和开发。
粒子群优化算法是一种基于群体的自适应搜索优化算法,存在后期收敛慢、搜索精度低、容易陷入局部极小等缺点,为此提出了一种改进的粒子群优化算法,从初始解和搜索精度两个方面进行了改进,提高了算法的计算精度,改善了算法收敛性,很大程度上避免了算法陷入局部极小.对经典函数测试计算,验证了算法的有效性。
关键词:粒子群优化算法;粒子群;优化技术;最佳位置;全局搜索;搜索精度Particle swarm optimization (PSO) algorithm is a novel evolutionary algorithm. It is a kind of stochastic global optimization technique. PSO finds optimal regions of complex search spaces through the interaction of individualsin a population of particles. The advantages of PSO lie in simple and powerful function. In this paper , classical particle swarm optimization algorithm , thepresent condition and some applications of the algorithms are introduced , and the possible research contents in future are also discussed.PSO is a population-based optimization technique proposed firstly for the above unconstrained minimization problem. In a PSO system, multiple candidate solutions coexist and collaborate simultaneously. Each solution called a ‘‘particle’’, flies in the problem sear ch space looking for the optimal position to land. A particle, as time passes through its quest, adjusts its position according to its own ‘‘experience’’ as well as the experience of neighboring particles. Tracking and memorizing the best position encountered build particle_s experience. For that reason, PSO possesses a memory (i.e. every particle remembers the best position it reached during the past). PSO system combines local search method(through self experience) with global search methods (through neighboring experience), attempting to balance explorationand exploitation.Abstract Particle Swarm Optimization Algorithm is a kind of auto-adapted search optimization based on community.But the standard particle swarm optimization is used resulting in slow after convergence, low search precision and easily leading to local minimum. A new Particle Swarm Optimization algorithm is proposed to improve from the initial solution and the search precision. The obtained results showed the algorithm computation precision and the astringency are improved,and local minimum is avoided. The experimental results of classic functions show that the improved PSO is efficientand feasible.Key words :particle swarm optimization algorithms ; unconstrained minimization problem;the bestposition;global search methods; the search precision目录一.引言二.PSO算法的基本原理和描述(一)概述(二)粒子群优化算法(三)一种改进型PSO算法——基于遗传交叉因子的粒子群优化算法简介1 自适应变化惯性权重2 交叉因子法(四) PSO与GA算法的比较1 PSO算法与GA算法2 PSO算法与GA算法的相同点3 PSO算法与GA算法的不同点三.PSO算法的实现及实验结果和仿真(一)基本PSO算法(二)算法步骤(三)伪代码描述(四)算法流程图(五)六个测试函数的运行结果及与GA算法结果的比较四结论五. 致谢六.参考文献一、引言混沌是一种有特点的非线形系统,它是一种初始时存在于不稳定的动态状态而且包含着无限不稳定时期动作的被束缚的行为。
方法的英文高级表达Advanced Expressions for Describing Methods1. Innovative Approach/Methodology:This cutting-edge method employs a unique and groundbreaking approach to tackle the problem at hand.2. Adaptive Strategy:This method is highly flexible and can be adjusted to fit different situations and circumstances.3. Unconventional Technique:This method adopts a non-traditional approach, deviatingfrom conventional methods to achieve superior results.4. Iterative Process:This method emphasizes a continuous and iterative approach, involving repeated cycles of testing and improvements.5. Holistic Approach:6. Rigorous Framework:This method follows a well-structured and rigorous framework, ensuring methodical and meticulous analysis.7. Cross-disciplinary Method:8. Data-driven Methodology:This method relies on extensive data analysis and interpretation to guide decision-making and problem-solving.9. Collaborative Approach:10. Agile Method:This method prioritizes adaptability and responsiveness to changing circumstances, allowing for quick adjustments and improvements.11. Systematic Procedure:This method follows a systematic and step-by-step procedure, ensuring a logical and coherent approach to problem-solving.12. Longitudinal Study:This method involves the collection and analysis of data over an extended period to observe patterns and trends.14. Randomized Control Trial (RCT):This method involves randomly assigning participants to different groups to test the effectiveness of an intervention or treatment.15. Qualitative Research:16. Quantitative Analysis:This method relies on numerical data and statistical techniques to measure and analyze relationships, trends, and patterns.17. Meta-analysis:18. Grounded Theory:This method seeks to generate new theories and concepts from qualitative data, allowing theories to emerge from the data itself.19. Action Research:This method involves implementing and evaluating interventions or changes within a real-world context, aiming to improve practices or solve practical problems.20. Monte Carlo Simulation:21. Genetic Algorithm:This method is an optimization technique inspired by the process of natural selection, using genetic operators to find the best solution among a set of possibilities.22. Neural Network:This method is an artificial intelligence model that attempts to mimic the structure and functioning of the brain, enabling pattern detection and prediction.23. Design Thinking:This method emphasizes empathy, creativity, and iterative problem-solving to create user-centered and innovative solutions.24. Six Sigma:This method is a data-driven approach focused on reducing defects and variability, aiming for near-perfect quality in products or processes.25. Lean Startup:This method advocates for rapid experimentation anditeration in the early stages of a business or project to minimize wasted resources and optimize success.These advanced expressions can help add precision and sophistication when describing methods in various contexts, such as research, problem-solving, and innovation.。
改进粒子群算法英文The particle swarm optimization (PSO) algorithm is a popular optimization technique that is inspired by thesocial behavior of birds flocking or fish schooling. It isa population-based stochastic optimization algorithm thatis commonly used to solve various optimization problems.The algorithm starts with a population of potential solutions, called particles, which move through the search space to find the optimal solution.There are several ways to improve the performance ofthe particle swarm optimization algorithm. One approach isto fine-tune the algorithm parameters, such as the inertia weight, acceleration coefficients, and population size, to better suit the specific problem being solved. Additionally, researchers have proposed various modifications to the standard PSO algorithm, such as incorporating local search techniques, hybridizing PSO with other optimization algorithms, and introducing adaptive mechanisms to dynamically adjust algorithm parameters during theoptimization process.Another avenue for improvement is the use of advanced techniques to handle constraints and multi-objective optimization problems within the PSO framework. This may involve the development of specialized constraint-handling mechanisms, such as penalty functions or repair operators, as well as the integration of Pareto-based approaches for handling multiple conflicting objectives.Furthermore, the performance of the PSO algorithm can be enhanced by addressing its limitations, such as premature convergence and poor exploration of the search space. This can be achieved through the development of diversity maintenance strategies, intelligentinitialization methods, and the incorporation of problem-specific knowledge to guide the search process.In addition to algorithmic improvements, the parallelization of the PSO algorithm can also lead to significant performance gains by harnessing the computational power of modern multi-core processors anddistributed computing environments.Overall, the continuous research and development efforts in the field of particle swarm optimization have led to a wide range of techniques and strategies for improving the algorithm's performance, robustness, and applicability to diverse optimization problems. By leveraging these advancements, practitioners and researchers can effectively apply the PSO algorithm to tackle complex real-world optimization challenges.。
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改进powell方法Powell method is a powerful optimization technique that aims to find the minimum of a function by iteratively searching and updating the search directions. Powell method has been widely used in various fields such as engineering, physics, and computer science due to its efficiency and simplicity.Powell方法是一种强大的优化技术,旨在通过迭代搜索和更新搜索方向来寻找函数的最小值。
由于其高效性和简单性,Powell方法已经在工程、物理学和计算机科学等各个领域得到了广泛的应用。
However, despite its popularity, Powell method still has some limitations and room for improvement. One of the main issues with Powell method is its convergence rate. In some cases, Powell method may converge slowly or fail to converge at all, which can be a significant drawback in practical applications.然而,尽管Powell方法很受欢迎,但它仍然存在一些局限性和改进空间。
Powell方法的一个主要问题是其收敛速度。
在某些情况下,Powell方法可能收敛缓慢甚至根本无法收敛,这在实际应用中可能是一个显著的缺点。
To improve the convergence rate of Powell method, researchers have proposed several enhancements and modifications. One common approach is to introduce line search techniques to fine-tune the step size in each iteration, which can accelerate the convergence and make Powell method more robust in finding the minimum of a function.为了提高Powell方法的收敛速度,研究人员提出了一些增强和修改方法。
1054Optimization TechniquesThis chapter provides information related to iSIGHT’s optimization techniques. Theinformation is divided into the following sections:“Introduction,” on page106 introduces iSIGHT’s optimization techniques.“Internal Formulation,” on page107 shows how iSIGHT approaches optimization.“Selecting an Optimization Technique,” on page112 lists all availableoptimization techniques in iSIGHT, divides them into subcategories, and definesthem.“Optimization Strategies,” on page121 outlines strategies that can be used to select optimization plans.“Optimization Tuning Parameters,” on page124 lists the basic and advanced tuning parameters for each iSIGHT optimization technique.“Numerical Optimization Techniques,” on page147 provides an in-depth look at various methods of direct and penalty numerical optimization techniques.Technique advantages and disadvantages are also discussed.“Exploratory Techniques,” on page175 discusses Adaptive Simulated Annealing and Multi-Island Genetic Algorithm optimization techniques.“Expert System Techniques,” on page178 provides a detailed look at iSIGHT’s expert system technique, Directed Heuristic Search (DHS), and discusses how itallows the user to set defined directions.“Optimization Plan Advisor,” on page190 provides details on how theOptimization Plan Advisor selects an optimization technique for a problem.“Supplemental References,” on page195 provides a listing of additionalreferences.106Chapter 4 Optimization TechniquesIntroductionThis chapter describes in detail the optimization techniques that iSIGHT uses, and howthey can be combined to conform to various optimization strategies. After you havechosen the optimization techniques that will best suit your needs, proceed to theOptimization chapter of the iSIGHT User’s Guide. This book provides instructions oncreating or modifying optimization plans. If you are a new user, it is recommended thatyou understand the basics of optimization plans and techniques before working withthe advanced features. Also covered in the iSIGHT User’s Guide are the various waysto control your optimization plan (e.g., executing tasks, stopping one task, stopping alltasks).Approximation models can be utilized during the optimization process to decreaseprocessing cost by minimizing the number of exact analyses. Approximation modelsare defined using the Approximations dialog box, or by loading a description file withpredefined models. Approximation models do not have to be initialized if they are usedinside an optimization Step. The optimizer will check and initialize the models, ifnecessary. For additional information on using approximation with optimization, seeChapter8 “Approximation Techniques”, or refer to the iSIGHT User’s Guide.iSIGHT combines the best features of existing exploitive and exploratory optimizationtechniques to supplement your knowledge about a given design problem. Exploitationis a feature of numerical optimization. It is the immediate focusing of the optimizer ona local region of the parameter space. All runs of the simulation codes are concentratedin this region with the intent of moving to better design points in the immediatevicinity. Exploration avoids focusing on a local region, but evaluates designsthroughout the parameter space in search of the global optimum.Domain-independent optimization techniques typically fall under three classes:numerical optimization techniques, exploratory techniques, and expert systems. Thetechniques described in this chapter are divided into these three categories.This chapter also provides information about optimization techniques including theirpurpose, their internal operations, and advantages and disadvantages of the techniques.For instructions on selecting a technique using the iSIGHT graphical user interface,refer to the iSIGHT User’s Guide.Internal Formulation 107Internal FormulationDifferent optimization packages use different mathematical formulas to achieveresults. The formulas shown below demonstrate how iSIGHT approaches optimization. The following are the key aspects to this formulation:All problems are internally converted to a single, weighted minimization problem.More than one iSIGHT parameter can make up the objective. Each individual objective has a weight multiplier to support objective prioritization, and a scale factor for normalization. If the goal of an individual objective parameter is maximization, then the weight multiplier gets an internal negative sign.If your optimization technique is a penalty-based technique, then the minimizationproblem is the same as described above with a penalty term added.Objective :MinimizeSubject to :Equality Constraints:Inequality Constraints:Design Variables: for integer and realor iSIGHT Input Parameter member of set S for discrete parametersWhere :SF = scale factor with a default of 1.0W = weight a default of 1.0W i SF i ---------i ∑F i ×x ()h k x ()T et arg –()W k SF k---------0k 1=;=×…K ,W j SF j---------LB g j x ()–()×0≤W j SF j ---------g j x ()UB –()×0j 1…L ,,=;≤LB SF -------iSIGHTInputParameter SF ------------------------------------------------------------------------------UB SF-------≤≤108Chapter 4 Optimization TechniquesThe penalty term is as follows:base + multiplier * summation of (constraint violation ** violation exponent)The default values for these parametesr are: 10.0 for penalty base, 1000.0 forpenalty multiplier, and 2 for the violation exponent. These defaults can beoverridden with Tcl API procedures discussed in the iSIGHT MDOL ReferenceGuide.All equality constraints, h(x), have a bandwidth of+-DeltaForEqualityConstraintViolation. This bandwidth allows a specified rangewithin which the constraint is not considered violated. The default bandwidth is.00001, and applies to all equality constraints. You can override this default withthe API procedure api_SetDeltaForEqualityConstraintViolation.All inequality constraints, g(x), are considered to be nonlinear. This setting cannot be overridden. If an iSIGHT output parameter has a lower and upper bound, thissetting is converted into two inequality constraints of the preceding form. Similarto the objective, each constraint can have a weight factor and scale factor.iSIGHT design variables, x, can be of type real, integer, or discrete. If the type is real or integer, the value must lie within user-specified lower and upper bounds. Ifno lower and upper bound is specified, the default value of 1E15 is used. Thisdefault can be overridden though the Parameters dialog box, or through the MDOLdescription file.There is one default bound for each optimization plan, and a common global(default) bound value (1E15). During the execution of an optimization plan, theplan's bound value overrides the common value. When no optimization plan isused, the default common value is used.The significance of the default bound is that, from the optimization techniquespoint of view, iSIGHT treats each design variable as if it has both a lower andupper bound. If the type is discrete, iSIGHT expects that the value of the variablewill always be one of the values provided in the user-supplied constraint set.Internally, iSIGHT will have a lower bound of 0, and an upper bound of n-1, wheren is the number of allowed values. The set of values can be supplied through theinterface, or through the API procedures api_SetInputConstraintAllowedValuesand api_AddInputConstraintAllowedValues. The optimization technique controlsthe values of the design variables, and iSIGHT expects the technique to insure thatthey are never allowed to violate their bounds.Internal Formulation109 To demonstrate the use of iSIGHT’s internal formulation, some simple modifications to the beamSimple.desc file can be made.Note:This description file can be found in the following location, depending on your operating system:UNIX and Linux:$(ISIGHT_HOME)/examples/doc_examplesWindows NT/2000/XP:$(ISIGHT_HOME)/examples_NT/doc_examplesMore information on this example can be found in the iSIGHT MDOL Reference Guide.For illustrative purposes, there are two objectives in this problem:minimize Deflectionminimize MassThe calculations shown in the following sections were done using the following values as parameters:BeamHeight = 40.0FlangeWidth = 40.0WebThickness = 1.0FlangeThichness = 1.0After executing a single run from Task Manager, the corresponding output values can be obtained:Mass = 118.0Deflection = 0.14286Stress = 21.82929110Chapter 4 Optimization TechniquesObjective FunctionAll optimization problems in iSIGHT are internally converted to a single, weightedminimization problem. More than one iSIGHT parameter can make up the objective.Each objective has a weight multiplier to support objective prioritization, and a scalefactor for normalization. If the goal of an individual objective parameter ismaximization, then the weight multiplier gets and internal negative sign.Hence, the calculation of the objective function in this example is the following:(Mass)*(Objective Weight)/(Objective Scale Factor) + (Deflection)*(ObjectiveWeight)/(Objective Scale Factor)Substituting the values for this problem, we get:(118.0)*(0.0078)/(1.0) + (0.14286)*(169.49)/(1.0) = 25.13158Penalty FunctionThe Penalty Function value is always computed by iSIGHT for constraint violations.The calculation of the penalty term is as follows:base + multiplier * (constraint violation violation exponent)Constraint violations are computed in one the following manners:For the Upper Bound (UB): For the Lower Bound (LB): For the Equality Constraint:(Value - UB)(UB Constraint Weight)UB Constraint Scale Factor(Value - LB)(LB Constraint Weight)LB Constraint Scale Factor(Value - Target)(Equality Constraint Weight) Equality Constraint Scale FactorInternal Formulation 111In iSIGHT, the default values of the base, multiplier, and violation exponent are as follows:Base = 10.0Multiplier = 1.0Exponent = 2Constraint Scale Factor = 1.0 Constraint Weight = 1.0In the following example, you want to maximize a displacement output variable, but it must be less than 16.0. Suppose the current displacement calculated is 30.5. Also assume that you set our UB Constraint weight to 3.0, and the UB Constraint Scale Factor to 10.0. The equations would appear as follows:ObjectiveAndPenalty FunctionThe ObjectiveAndPenalty variable in iSIGHT is then just the sum of the Objective function value and the Penalty term. In this example, the ObjectiveAndPenalty variable would have the following value:25.13138 + 33990.645 = 34015.777It is the value of this variable that is used to set the value of the Feasibility variable in iSIGHT. Recall that the Feasibility variable in iSIGHT is what alerts you to feasible versus non-feasible runs. For more information on feasibility, see “iSIGHT Supplied Variables,” on page 101.Penalty = 10.0 + 1.0 * (30.5 - 16.0) * 3.010.02112Chapter 4 Optimization TechniquesSelecting an Optimization TechniqueThis section provides instructions that explain how to select an optimization plan. Akey part of this process is selecting an optimization technique. It provides an overviewof the techniques available in iSIGHT, and provides examples of which techniques youmay want to select based on certain types of design problems.Note:The following is intended to provide general information and guidelines.However, it is highly recommended that you experiment with several techniques, andcombinations of techniques, to find the best solution.Optimization Technique CategoriesThe optimization techniques in iSIGHT can be divided into three main categories:Numerical Optimization TechniquesExploratory TechniquesExpert System TechniquesThe techniques which fall under these three categories are outlined in the followingsections, and are defined in “Optimization Technique Descriptions,” on page114.Numerical Optimization TechniquesNumerical optimization techniques generally assume the parameter space is unimodal,convex, and continuous. The techniques including in iSIGHT are:ADS-based TechniquesExterior Penalty (page115)Modified Method of Feasible Directions (page116)Sequential Linear Programming (page117)Generalized Reduced Gradient - LSGRG2 (page115)Hooke-Jeeves Direct Search Method (page115)Selecting an Optimization Technique113 Method of Feasible Directions - CONMIN (page115)Mixed Integer Optimization - MOST (page116)Sequential Quadratic Programming - DONLP (page117)Sequential Quadratic Programming - NLPQL (page117)Successive Approximation Method (page117)The numerical optimization techniques can be further divided into the following two categories:direct methodspenalty methodsDirect methods deal with constraints directly during the numerical search process. Penalty methods add a penalty term to the objective function to convert a constrained problem to an unconstrained problem.Direct Methods Penalty MethodsGeneralized Reduced Gradient - LSGRG2Exterior PenaltyMethod of Feasible Directions - CONMIN Hooke-Jeeves Direct SearchMixed Integer Optimization - MOSTModified Method of Feasible Directions - ADSSequential Linear Programming - ADSSequential Quadratic Programming - DONLPSequential Quadratic Programming - NLPQLSuccessive Approximation MethodExploratory TechniquesExploratory techniques avoid focusing only on a local region. They generally evaluate designs throughout the parameter space in search of the global optimum. The techniques included in iSIGHT are:Adaptive Simulated Annealing (page114)Multi-Island Genetic Algorithm (page116)114Chapter 4 Optimization TechniquesExpert System TechniquesExpert system techniques follow user defined directions on what to change, how tochange it, and when to change it. The technique including in iSIGHT is DirectedHeuristic Search (DHS) (page114).Optimization Technique DescriptionsThe following sections contain brief descriptions of each technique available iniSIGHT.Adaptive Simulated AnnealingThe Adaptive Simulated Annealing (ASA) algorithm is very well suited for solvinghighly non-linear problems with short running analysis codes, when finding the globaloptimum is more important than a quick improvement of the design.This technique distinguishes between different local optima. It can be used to obtain asolution with a minimal cost, from a problem which potentially has a great number ofsolutions.Directed Heuristic SearchDHS focuses only on the parameters that directly affect the solution in the desiredmanner. It does this using information you provide in a Dependency Table. You canindividually describe each parameter and its characteristics in the Dependency Table.Describing each parameter gives DHS the ability to know how to move each parameterin a way that is consistent with its order of magnitude, and with its influence on thedesired output. With DHS, it is easy to review the logs to understand why certaindecisions were made.Selecting an Optimization Technique115 Exterior PenaltyThis technique is widely used for constrained optimization. It is usually reliable, and has a relatively good chance of finding true optimum, if local minimums exist. The Exterior Penalty method approaches the optimum from infeasible region, becoming feasible in the limit as the penalty parameter approaches ∞(γp→∞). Generalized Reduced Gradient - LSGRG2This technique uses generalized reduced gradient algorithm for solving constrained non-linear optimization problems. The algorithm uses a search direction such that any active constraints remain precisely active for some small move in that direction. Hooke-Jeeves Direct Search MethodThis technique begins with a starting guess and searches for a local minimum. It does not require the objective function to be continuous. Because the algorithm does not use derivatives, the function does not need to be differentiable. Also, this technique has a convergence parameter, rho, which lets you determine the number of function evaluations needed for the greatest probability of convergence.Method of Feasible Directions - CONMINThis technique is a direct numerical optimization technique that attempts to deal directly with the nonlinearity of the search space. It iteratively finds a search direction and performs a one dimensional search along this direction. Mathematically, this can be expressed as follows:Design i = Design i-1 + A * Search Direction iIn this equation, i is the iteration, and A is a constant determined during the one dimensional search.The emphasis is to reduce the objective while maintaining a feasible design. This technique rapidly obtains an optimum design and handles inequality constraints. The technique currently does not support equality constraints.116Chapter 4 Optimization TechniquesMixed Integer Optimization - MOSTThis technique first solves the given design problem as if it were a purely continuousproblem, using sequential quadratic programming to locate an initial peak. If all designvariables are real, optimization stops here. Otherwise, the technique will branch out tothe nearest points that satisfy the integer or discrete value limits of one non-realparameter, for each such parameter. Those limits are added as new constraints, and thetechnique re-optimizes, yielding a new set of peaks from which to branch. As theoptimization progresses, the technique focuses on the values of successive non-realparameters, until all limits are satisfied.Modified Method of Feasible DirectionsThis technique is a direct numerical optimization technique used to solve constrainedoptimization problems. It rapidly obtains an optimum design, handles inequality andequality constraints, and satisfies constraints with high precision at the optimum.Multi-Island Genetic AlgorithmIn the Multi-Island Genetic Algorithm, as with other genetic algorithms, each designpoint is perceived as an individual with a certain value of fitness, based on the value ofobjective function and constraint penalty. An individual with a better value of objectivefunction and penalty has a higher fitness value.The main feature of Multi-Island Genetic Algorithm that distinguishes it fromtraditional genetic algorithms is the fact that each population of individuals is dividedinto several sub-populations called “islands.” All traditional genetic operations areperformed separately on each sub-population. Some individuals are then selected fromeach island and migrated to different islands periodically. This operation is called“migration.” Two parameters control the migration process: migration interval, whichis the number of generations between each migration, and migration rate, which is thepercentage of individuals migrated from each island at the time of migration.Selecting an Optimization Technique117 Sequential Linear ProgrammingThis technique uses a sequence of linear sub-optimizations to solve constrained optimization problems. It is easily coded, and applicable to many practical engineering design problems.Sequential Quadratic Programming - DONLPThis technique uses a slightly modified version of the Pantoja-Mayne update for the Hessian of the Lagrangian, variable scaling, and an improved armijo-type stepsize algorithm. With this technique, bounds on the variables are treated in a projected gradient-like fashion.Sequential Quadratic Programming - NLPQLThis technique assumes that objective function and constraints are continuously differentiable. The idea is to generate a sequence of quadratic programming subproblems, obtained by a quadratic approximation of the Lagrangian function, and a linearization of the constraints. Second order information is updated by aquasi-Newton formula, and the method is stabilized by an additional line search. Successive Approximation MethodThis technique lets you specify a nonlinear problem as a linearized problem. It is a general program which uses a Simplex Algorithm in addition to sparse matrix methods for linearized problems. If one of the variables is declared an integer, the simplex algorithm is iterated with a branch and bound algorithm until the desired optimal solution is found. The Successive Approximation Method is based on the LP-SOLVE technique developed by M. Berkalaar and J.J. Dirks.Optimization Technique SelectionTable5-1 on page119, suggests some of the optimization techniques you may want to try based on certain characteristics of your design problem. Table5-2 on page120, suggests some of the optimization techniques you may want to try, based on certain characteristics of the optimization technique.118Chapter 4 Optimization TechniquesThe following abbreviations are used in the tables:Note : In the tables, similar techniques are represented by the same abbreviation/column. That is, the MMFD column represents the Modified Method of Feasible Directions techniques - ADS and the Method of Feasible Directions -CONMIN. The SQP column represents both DONLP and NLPQL versions of sequential quadratic programming.Optimization MethodsAbbreviation Modified Method of Feasible Directions - ADSMethod of Feasible Directions - CONMINMMFDSequential Linear Programming - ADSSLP Sequential Quadratic Programming - DONLPSequential Quadratic Programming - NLPQLSQP Hooke-Jeeves Direct Search MethodHJ Successive Approximation MethodSAM Directed Heuristic Search (DHS)DHS Multi-Island Genetic AlgorithmGA Mixed Integer Optimization - MOSTMOST Generalized Reduced Gradient - LSGRG2 LSGRG2Selecting an Optimization Technique 119* This is only true for NLPQL. DONLP does not handle user-supplied gradients.** Although the application may require some or all variables to be integer or discrete, the task process must be able to evaluate arbitrary real-valued design points.Table 5-1. Selecting Optimization Techniques Based on Problem Characteristics ProblemCharacteristicsPen Meth MMFD SLP SQP HJ SAM DHS GA Sim.Annl.MOST LSGRG2Only realvariablesx x x x x x x x x x**x Handles unmixedor mixedparameters oftypes real, integer,and discretex x x x x x Highly nonlinearoptimizationproblemx x x Disjointed designspaces (relativeminimum)x x x Large number ofdesign variables(over 20)x x x x x x Large number ofdesign constraints(over 1000)x x x x Long runningsimcodes/analysis(expensivefunctionevaluations)x x x x x Availability ofuser-suppliedgradients x x x x*x x120Chapter 4 Optimization TechniquesTable 5-2. Selecting Optimization Techniques Based on Technique CharacteristicsTechnique Characteristics MMFD SQP HJ SAM DHS GA SimulatedAnnealingMOST LSGRG2Does not requirethe objectivefunction to becontinuousx x x x xHandlesinequality andequalityconstraintsx*x x x x x x x xFormulationbased onKuhn-Tuckeroptimalityconditionsx x xSearches from aset of designsrather than asingle designx x**Used probabilisticrulesx xGets betteranswers at thebeginningxDoes not assumeparameterindependencex x x xDoes not need touse finitedifferencesx x x xOptimization Strategies121* This is only true for the Modified Method of Feasible Directions - ADS. The Method of Feasible Directions - CONMIN does not handle equality constraints.** First finds an initial peak from a single design, then searches a set of designs derived from that peak.Optimization StrategiesYou can specify a single optimization technique or a sequence of techniques, for a particular design optimization run. iSIGHT searches the design space by virtue of the behavior of the techniques, guided and bounded by any design variables and any constraints specified. Upon completion of an optimization run, you can switch techniques by either selecting another plan created with different techniques, or by modifying the current plan to apply a different optimization technique.Optimization plans with more than one technique can use strategies for combining the multiple optimization techniques. The following sections defines six optimization strategies.TechniqueCharacteristics MMFD SQP HJ SAM DHS GA Simulated Annealing MOST LSGRG2Not sensitive todesign variablevalues withdifferent orders ofmagnitudex x x Is easy tounderstandx Can be configuredso it searches in acontrolled, orderlyfashion xTable 5-2. Selecting Optimization Techniques Based on Technique Characteristics (cont.)122Chapter 4 Optimization TechniquesIn theory and in practice, a combination of techniques usually reflects an optimizationstrategy to perform one or more of the following objectives:“Coarse-to-Fine Search” on this page“Establish Feasibility, Then Search Feasible Region” on this page“Exploitation and Exploration,” on page123“Complementary Landscape Assumptions,” on page123“Procedural Formulation,” on page123“Rule-Driven,” on page124These multiple-technique optimization strategies are important to understand, and canserve as guidelines for those new to the iSIGHT optimization environment, or todesign engineers who are not optimization experts.Coarse-to-Fine SearchThe coarse-to-fine search strategy typically involves using the same optimizationtechnique more than once. For example, you may have defined a plan using theSequential Quadratic Programming technique twice, with the first instance calledSQP1 and the second instance called SQP2. The first instance, SQP1, may have a largefinite difference Step size, while the second instance, SQP2, may have a small finitedifference Step size.Advanced iSIGHT users may extend the coarse-to-fine search plan further bymodifying the number of design variables and constraints used in the search process,through the use of plan prologues and epilogues.Establish Feasibility, Then Search FeasibleRegionSome optimization techniques are most effective when started in the feasible region ofthe design space, while others are most effective when started in the infeasible region.If you are uncertain whether the initial design point is feasible or not, then anoptimization plan consisting of a penalty method, followed by a direct method,provides a general way to handle any problem.Optimization Strategies123 Advanced iSIGHT users can enhance the prologue at the optimization plan level so that the penalty technique is only invoked if the design is infeasible.Exploitation and ExplorationNumerical techniques are exploitive, and quickly climb to a local optimum. An optimization plan consisting of an exploitive technique followed by an explorative technique, such as a Multi-Island Genetic Algorithm or Adaptive Simulated Annealing, then followed up with another exploitive technique, serves a particular purpose. This strategy can quickly climb to a local optimum, explore for a promising region of the design space, then exploit.Advanced iSIGHT users can define a control section to allow the exploitive and explorative techniques to loop between each other.Complementary Landscape AssumptionsEach optimization technique makes assumptions about the underlying landscape in terms of continuity and modality. In general, numerical optimization assumes a continuous, unimodal design space. On the other hand, exploratory techniques work with mixed discrete and real parameters in a discontinuous space.If you are not sure of the underlying landscape, or if the landscape contains both discontinuities and integers, then you should develop a plan composed of optimization techniques from both categories.Procedural FormulationMany designers approach a design optimization problem by conducting a series of iterative, trial-and-error problem formulations to find a solution. Typically, the designer will vary a set of design variables and, depending on the outcome, change the problem formulation then run another optimization. This process is continued until a satisfactory answer is reached.If the series of formulations becomes somewhat repetitive and predictable, then the advanced iSIGHT user can automate this process by programming the formulation and。
中图分类号:V211.3 论文编号:1028701 18-B061 学科分类号:080103博士学位论文叶轮机械非定常流动及气动弹性计算研究生姓名周迪学科、专业流体力学研究方向气动弹性力学指导教师陆志良教授南京航空航天大学研究生院航空宇航学院二О一八年十月Nanjing University of Aeronautics and AstronauticsThe Graduate SchoolCollege of Aerospace EngineeringNumerical investigations of unsteady aerodynamics and aeroelasticity ofturbomachinesA Thesis inFluid MechanicsbyZhou DiAdvised byProf. Lu ZhiliangSubmitted in Partial Fulfillmentof the Requirementsfor the Degree ofDoctor of PhilosophyOctober, 2018南京航空航天大学博士学位论文摘要气动弹性问题是影响叶轮机械特别是航空发动机性能和安全的一个重要因素。
作为一个交叉学科,叶轮机械气动弹性力学涉及与叶片变形和振动相关联的定常/非定常流动特性、颤振机理以及各种气弹现象的数学模型等的研究。
本文基于计算流体力学(CFD)技术自主建立了一个适用于叶轮机械定常/非定常流动、静气动弹性和颤振问题的综合计算分析平台,并针对多种气动弹性问题进行了数值模拟研究。
主要研究内容和学术贡献如下:由于叶轮机械气动弹性与内流空气动力特性密切相关,真实模拟其内部流场是研究的重点之一。
基于数值求解旋转坐标系下的雷诺平均N–S(RANS)方程,首先构造了适合于旋转机械流动的CFD模拟方法。
特别的,针对叶片振动引起的非定常流动问题,采用动网格方法进行模拟,通过一种高效的RBF–TFI方法实现网格动态变形;针对动静叶排干扰引起的非定常流动问题,采用一种叶片约化模拟方法,通过一种基于通量形式的交界面参数传递方法实现转静子通道之间流场信息的交换。
1、Which of the following is NOT a type of cloud computing service model?A. Software as a Service (SaaS)B. Platform as a Service (PaaS)C. Infrastructure as a Service (IaaS)D. Data as a Product (DaaP)(答案) D2、In computer networking, what does HTTP stand for?A. HyperText Transfer ProtocolB. HighText Transfer ProcessC. HyperText Transmission ProtocolD. HighText Transmission Process(答案) A3、Which of these is a programming language primarily used for web development, known for its ease of use and readability?A. JavaB. PythonC. PHPD. Ruby(答案) C4、What is the main purpose of a firewall in a computer network?A. To provide a secure connection between two networksB. To encrypt data transmissionsC. To store data temporarilyD. To manage user accounts(答案) A5、Which of the following is a search engine optimization (SEO) technique focused on improving the website's content and structure to make it more attractive to search engines?A. Link buildingB. Keyword stuffingC. On-page SEOD. Off-page SEO(答案) C6、In database management, what does SQL stand for?A. Structured Query LanguageB. Standard Query LanguageC. Simple Query LanguageD. Sequential Query Language(答案) A7、Which of the following is a popular open-source content management system (CMS) used for building websites and blogs?A. WordPressB. Windows ServerC. WixD. Weebly(答案) A8、What is the term used to describe the process of converting analog signals into digital signals?A. ModulationB. DemodulationC. DigitizationD. Encoding(答案) C。
Optimization AlgorithmsOptimization algorithms are a crucial tool in various fields, ranging from engineering and finance to healthcare and logistics. These algorithms aim to find the best solution to a given problem by minimizing or maximizing a certain objective function, subject to a set of constraints. They are used to improve efficiency, reduce costs, and enhance decision-making processes. However, the effectiveness of an optimization algorithm depends on various factors, such as the problem complexity, the quality of the initial solution, and the algorithm's convergence properties. One of the key challenges in optimization is the trade-off between exploration and exploitation. Exploration involves searching for new solutions in the solution space, while exploitation focuses on refining the current best solution. Balancing these two aspects is crucial for finding the global optimum rather than getting stuck in a local optimum. This is especially important in complex optimization problems with multiple local optima, where traditional algorithms may struggle to converge to the best solution. Evolutionary algorithms, such as genetic algorithms and particle swarm optimization, are popular optimization techniques inspired by natural evolution. These algorithms mimic the process of natural selection, where the fittest individuals are selected for reproduction and produce offspring with improved characteristics. By combining exploration and exploitation, evolutionary algorithms can effectively search the solution space and find high-quality solutions to complex optimization problems. They are particularly well-suited for problems with non-linear and non-convex objective functions, where traditional gradient-based methods may fail. Another important aspect of optimization algorithms is their scalability and efficiency. As the size of the problem increases, the algorithm's computational complexity also grows, making it challenging to find an optimal solution within a reasonable amount of time. Metaheuristic algorithms, such as simulated annealing and tabu search, are designed to handle large-scale optimization problems by balancing exploration and exploitation efficiently. These algorithms use stochastic search strategies to escape local optima and converge to the global optimum in a reasonable amount of time. In recent years, machine learning techniques, such as deep learning andreinforcement learning, have been applied to optimization problems with great success. These techniques leverage the power of neural networks and learning algorithms to adaptively search the solution space and improve the optimization process iteratively. Deep reinforcement learning, in particular, has shown promising results in solving complex optimization problems with high-dimensional solution spaces. By combining the strengths of optimization algorithms and machine learning, researchers can develop more robust and efficient optimizationtechniques for real-world applications. Overall, optimization algorithms play a crucial role in solving complex problems across various domains. By balancing exploration and exploitation, leveraging evolutionary and metaheuristic algorithms, and incorporating machine learning techniques, researchers can develop powerful optimization tools to tackle real-world challenges effectively. As technology continues to advance, optimization algorithms will continue to evolve and improve, enabling us to find optimal solutions to increasingly complex problems.。