共查询到17条相似文献，搜索用时 491 毫秒
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常规DCD(dynamic canonical descent)算法具有全局优化能力且无需考虑目标函数的可微性,只要预先定义优化空间即可,但是该算法的收敛速度有限,为此提出了变参数DCD算法,并建立了其相应的算法迭代格式。在数值试验和工程应用中将该变参数DCD算法与常规的DCD算法进行比较,其结果均表明：变参数DCD算法在全局优化能力和收敛速度上找到了一个均衡点,该算法不仅具有DCD算法的全局优化能力,而且收敛时所需的目标函数评估次数少,在优化过程中该算法展示出了稳定性强且优化结果可靠度高的一面。 相似文献
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应用迭代法的困难之处在于：一是迭代公式是否存在导数运算；二是初始值选定是否影响迭代公式的收敛性；三是收敛快速和达到需要精度等问题。我们获得了求方程根不用计算导数的平方收敛迭代公式，并设计了求根的大范围收敛算法，编写了C^ 语言程序，进行了算法和数值分析。与其它算法比较，该算法具有无导数计算、初值任意选定、平方快速收敛、大范围收敛和双精度控制(根的精度和函数值的精度控制)等优点。 相似文献
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有限元直接迭代算法 总被引：10，自引：1，他引：9
陈小斌 《物探化探计算技术》1999,21(2):165-171
本文提出了有限元直接迭代算法。推导出了直接迭代格式的统一公式,并给出了几种网格剖分方式下的有限元直接迭代格式的具体形式,讨论了各剖分方式的优劣。编程实现了该算法并进行了实例验证 相似文献
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基于粒子群优化的岩土工程反分析研究 总被引：11，自引：0，他引：11
岩土工程优化反分析本质上看是一个典型的复杂非线性函数优化问题,采用全局优化算法是解决这个问题的理想途径,但由于优化反分析中多次调用正分析的特点使得整个算法的计算效率很低。为了提高优化反分析的计算效率,把一种计算效率更高的新型仿生算法--粒子群优化引入岩土工程反分析领域,提高反分析的计算效率。在此基础上,结合有限元数值分析技术,提出了一种新的岩土工程优化反分析算法--粒子群优化反分析。并通过一个简单算例验证了该法的有效性。 相似文献
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基于交替迭代算法神经网络评价岩石边坡稳定性 总被引：2，自引：0，他引：2
目前边坡工程中常用的稳定性分析方法主要分为极限平衡法和数值分析法2大类，文章对它们各自的主要愿理、特点及其优缺点等进行了阐述。首先，根据经典边坡稳定分析方法存在的局限性，提出有必要建立基于人工神经网络的边坡稳定性预报方法。其次，针对经典算法BP网络存在的某些缺陷，提出了一种交替迭代算法神经网络，以提高其非线性映射能力和泛化能力。交替迭代神经网络算法通过解2个阶数比较低的线性代数方程组，逐步求得连接权值的。以此提高收敛速度，且有利于寻求最优解。作者用FORTRAN语言编制了程序。分析了建立边坡岩体稳定性预测网络模型的建立中应该注意的几个方面。最后，基于已有的40个岩石边坡工程实例进行所建立的神经网络的训练和边坡稳定的预报，结果表明文中所建立的边坡稳定性预报方法具有较高的预报准确度。 相似文献
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本文提出一种新的地质总体分解计算方法－线性、非线性分部迭代算法。实例表明，该方法计算精度高、收敛快。 相似文献
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有限元直接迭代算法中一种基本结构的研究 总被引：4，自引：0，他引：4
陈小斌 《物探化探计算技术》2000,22(2):132-136
首先探讨了基本结构的含义,提出笛卡尔直角坐标系下具体的基本结构,讨论了该基本结构的各种剖分方式;然后,基于有限元直接迭代算法原理,具体推导了其中一种剖分方式下的迭代格式;最后在正方形网格部分情形下,以拉普拉斯方程为例,分析了各部分方式所确定的迭代格式的截断误差。 相似文献
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位移反分析的粒子群优化-高斯过程协同优化方法 总被引：2，自引：0，他引：2
针对采用随机全局优化技术进行岩土工程位移反分析存在数值计算量大、效率低的问题,将粒子群优化算法与高斯过程机器学习技术相结合,提出了位移反分析的粒子群优化-高斯过程协同优化方法。该方法利用全局寻优性能优异的粒子群优化算法进行寻优的基础上,采用高斯过程机器学习模型不断地总结历史经验,预测包含全局最优解的最有前景区域,通过提高粒子群搜索效率并降低适应度评价次数,进而有效地降低位移反分析过程中的数值计算工作量。多种测试函数的数学验证和工程算例的研究结果表明该方法是可行的,与传统方法相比较,可显著地降低位移反分析的计算耗时。 相似文献
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弹性波模拟或逆时偏移时,对空间偏导数采用高阶差分格式可提高计算精度,但这种算法的稳定性条件过于严格,要求差分离散的时间步长必须足够小以确保算法稳定。在常规空间高阶差分格式的基础上,将速度(应力)对时间的高阶导数转化为不同精度的应力(速度)对空间的差分,得到了一种新的基于交错网格的时间高阶、空间高阶差分格式。通过对交错网格时间高阶差分格式稳定性的分析,认为该算法的稳定性条件较常规算法宽松,在弹性波场的求解过程中可以采用更大的时间步长。 相似文献
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This paper presents a reformulation of the original Matsuoka–Nakai criterion for overcoming the limitations which make its use in a stress point algorithm problematic. In fact, its graphical representation in the principal stress space is not convex as it comprises more branches, plotting also in negative octants, and it does not increase monotonically as the distance of the stress point from the failure surface rises. The proposed mathematical reformulation plots as a single, convex surface, which entirely lies in the positive octant of the stress space and evaluates to a quantity which monotonically increases as the stress point moves away from the failure surface. It is an exact reproduction, and not an approximated one, of the only significant branch of the original criterion. It is also suitable for shaping in the deviatoric plane the yield and plastic potential surfaces of complex constitutive models. A very efficient numerical algorithm for the implicit integration of the proposed formulation is presented, which enables the evaluation of the stress at the end of each increment by solving a single scalar equation, both for associated and non‐associated plasticity. The algorithm can be easily adapted for other smooth surfaces with linear meridian section. Finally, a close expression of the consistent Jacobian matrix is given for achieving quadratic convergence in the external structural newton loop. It is shown that all this results in extremely fast solutions of boundary value problems. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
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利用模拟退火反演过程中废弃的随机搜索的模型解信息, 建立起目标函数的二阶趋势面, 根据趋势面的分析结果预测和指导模拟退火的搜索方向, 使模拟退火法快速地收敛到全局最优解.数值试验证明, 这种方法比传统模拟退火法的计算效率高出十倍以上. 相似文献
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Slope stability analysis of soil with a weak layer sandwiched between two strong layers is considered as a complex geotechnical problem. In this problem, the objective function is non‐convex and discontinuous with the presence of multiple strong local minima. Classical optimization techniques fail to converge to a valid solution unless a proper initial trial is adopted. Even though many new optimization algorithms have emerged, they have not been applied to geotechnical problems yet. In the present study, some recent swarm intelligence algorithms are adopted for some complicated example of slope stability problems and benchmarked with the traditional particle swarm optimization algorithm. From the results, it seems the levy flight krill herd algorithm is the most efficient method over proposed algorithms for this kind of problem. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
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Paul Schiffelbein 《Mathematical Geology》1985,17(7):703-728
A number of problems in geology can be formulated so that they consist of optimizing a real-valued function (termed the objective function) on some interval or over some region. Many methods are available for solution if the function is unimodal within the domain of interest. Direct methods, involving only function evaluations, are particularly useful in geological problems where the objective function may be strongly nonlinear and constructed from sampled data. In practical problems, the objective function often is not unimodal. Standard optimization routines are not capable of distinguishing between local extrema or of locating the global extremum, which is the point of interest in most cases. The usual approach—trying several different starting points in the hope that the best local extremum found is the global extremum—is inefficient and unreliable. An ancillary algorithm has been developed which avoids these problems and which couples with a variety of local optimization routines. The algorithm first constructs a grid of objective function values over some feasible region. The region dimensions and grid spacings are based on specific problem considerations. First differences are then calculated for successive points along each grid line and monitored in sign only, which rapidly locates extrema. User interaction determines how many of these extrema will undergo further investigation, which is carried out by passing locations to a local optimization subroutine. The algorithm has proved successful on a number of problems. A geological example—determination of benthic mixing parameters in deep-sea sediments via minimization of stratigraphic offset between
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O signals from two different species of planktonic foraminifera—is given. FORTRAN code is provided for the global optimization routine, a golden section search subroutine for one-dimensional objective functions, and a simplex subroutine for multidimensional problems. 相似文献
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Displacement back analysis is a common method to identify mechanical geo‐material parameters using the monitored displacement. How to obtain a global optimum solution in large space search of highly non‐linear multimodal is a key point of optimum back analysis. The paper presents a new back analysis that is an integration of evolutionary support vector machines (SVMs), numerical analysis and genetic algorithm. The non‐linear relationship between the mechanical geo‐material parameters to be identified and the corresponding displacement values of key points is learned and represented by evolutionary SVMs in global optimum. Numerical analysis is used to create training and testing samples for recognition of SVMs. Then, performing a global optimum search on the obtained SVMs using genetic algorithm can identify the mechanical geo‐material parameters. The proposed algorithm is tested by back analysis of an elastic plate and an elastic–plastic plate and used to recognize mechanical parameters of subclay, strongly weathered tuff and weakly weathered tuff of Bachimen slope, Funing expressway, Fujian, China. The results indicate that applicability of the proposed algorithm with enough accuracy. Copyright © 2004 John Wiley & Sons, Ltd. 相似文献
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Slope stability optimization, in the presence of a band of a weak layer between two strong layers, is accounted for in complicated geotechnical problems. Classical optimization algorithms are not suitable for solving such problems as they need a proper preliminary solution to converge to a valid result. Therefore, it is necessary to find a proper algorithm which is capable of finding the best global solution. Recently a lot of metaheuristic algorithms have been proposed which are able to evade local minima effectively. In this study four evolutionary algorithms, including well‐known and recent ones, such as genetic algorithm, differential evolution, evolutionary strategy and biogeography‐based optimization (BBO), are applied in slope stability analysis and their efficiencies are explored by three benchmark case studies. Result show BBO is the most efficient among these evolutionary algorithms and other proposed algorithms applied to this problem. Copyright © 2016 John Wiley & Sons, Ltd. 相似文献