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Modified genetic algorithm with simulated annealing applied to optimal load dispatch of the Three Gorges Hydropower Plant in China 总被引：1，自引：0，他引：1
A method is presented for optimal load dispatch in large-scale hydropower plants using a genetic algorithm integrated with simulated annealing. The genetic algorithm overcomes dependence on an initial value and provides parallel processing and fast convergence, whereas simulated annealing prevents prematurity and retrieving of the local instead of the global optimum. Thus, the integrated genetic-simulated annealing algorithm improves efficiency and robustness to obtain solutions close to the global optimum. We evaluated the proposed algorithm to determine the optimal load dispatch of 32 units of the Three Gorges Hydropower Plant in China. Test results show that the minimum water consumption obtained using the proposed algorithm is similar to the optimum obtained from a previously proposed “improved” genetic algorithm when the total load of the plant is relatively high. However, for reduced load, the proposed algorithm clearly outperforms the “improved” genetic algorithm. 相似文献
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针对地震静校正存在的非线性和多参数性等问题，本文提出了一种全局优化的剩余静校正方法.在模型更新规则上，把模拟退火方法和均匀优化设计方法结合在一起，使模型的更新更为合理，从而加快了搜寻全局最优解的速度.同时提出了模型的不同分量的温度参数和退火过程的选取方式，把温度参数的选取与地震剖面的能量联系起来，使温度参数的选取具有自适应的特点.该方法克服了常规模拟退火方法所具有的寻优空间不均匀以及退火参数需通过多次试验选取的缺陷.通过实际地震资料的计算证明，本文提出的方法对地震静校正问题合理而有效. 相似文献
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Comparing the performances of four stochastic optimisation methods using analytic objective functions, 1D elastic full‐waveform inversion,and residual static computation 
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下载免费PDF全文 Angelo Sajeva Mattia Aleardi Bruno Galuzzi Eusebio Stucchi Emmanuel Spadavecchia Alfredo Mazzotti 《Geophysical Prospecting》2017,65(Z1):322-346
We compare the performances of four stochastic optimisation methods using four analytic objective functions and two highly non‐linear geophysical optimisation problems: one‐dimensional elastic full‐waveform inversion and residual static computation. The four methods we consider, namely, adaptive simulated annealing, genetic algorithm, neighbourhood algorithm, and particle swarm optimisation, are frequently employed for solving geophysical inverse problems. Because geophysical optimisations typically involve many unknown model parameters, we are particularly interested in comparing the performances of these stochastic methods as the number of unknown parameters increases. The four analytic functions we choose simulate common types of objective functions encountered in solving geophysical optimisations: a convex function, two multi‐minima functions that differ in the distribution of minima, and a nearly flat function. Similar to the analytic tests, the two seismic optimisation problems we analyse are characterised by very different objective functions. The first problem is a one‐dimensional elastic full‐waveform inversion, which is strongly ill‐conditioned and exhibits a nearly flat objective function, with a valley of minima extended along the density direction. The second problem is the residual static computation, which is characterised by a multi‐minima objective function produced by the so‐called cycle‐skipping phenomenon. According to the tests on the analytic functions and on the seismic data, genetic algorithm generally displays the best scaling with the number of parameters. It encounters problems only in the case of irregular distribution of minima, that is, when the global minimum is at the border of the search space and a number of important local minima are distant from the global minimum. The adaptive simulated annealing method is often the best‐performing method for low‐dimensional model spaces, but its performance worsens as the number of unknowns increases. The particle swarm optimisation is effective in finding the global minimum in the case of low‐dimensional model spaces with few local minima or in the case of a narrow flat valley. Finally, the neighbourhood algorithm method is competitive with the other methods only for low‐dimensional model spaces; its performance sensibly worsens in the case of multi‐minima objective functions. 相似文献
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Si-Yu Li Shu-Ming Wang Peng-Fei Wang Xiao-Lu Su Xin-Song Zhang Zhi-Hui Dong 《Acta Geophysica》2018,66(4):607-621
The grey wolf optimizer (GWO) is a novel bionics algorithm inspired by the social rank and prey-seeking behaviors of grey wolves. The GWO algorithm is easy to implement because of its basic concept, simple formula, and small number of parameters. This paper develops a GWO algorithm with a nonlinear convergence factor and an adaptive location updating strategy and applies this improved grey wolf optimizer (improved grey wolf optimizer, IGWO) algorithm to geophysical inversion problems using magnetotelluric (MT), DC resistivity and induced polarization (IP) methods. Numerical tests in MATLAB 2010b for the forward modeling data and the observed data show that the IGWO algorithm can find the global minimum and rarely sinks to the local minima. For further study, inverted results using the IGWO are contrasted with particle swarm optimization (PSO) and the simulated annealing (SA) algorithm. The outcomes of the comparison reveal that the IGWO and PSO similarly perform better in counterpoising exploration and exploitation with a given number of iterations than the SA. 相似文献
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利用水平与竖向谱比（HVSR）方法反演场地速度结构是国际上迅速发展的研究领域.HVSR反演计算实质是一个土层场地模型空间搜索的全局优化问题,当模型搜索空间的复杂程度增大时,目前常用的搜索算法收敛速度慢,计算效率较低.本文实现了一种结合遗传和模拟退火方法优点的混合全局优化HVSR反演算法,通过理论模型和竖向台阵实测数据的检验,表明该算法能获得很好的反演效果,较好地解决了蒙特卡罗方法收敛速度慢,遗传算法收敛早熟和模拟退火算法搜索效率低的问题.本文在此基础上讨论了单台加速度S波记录用于场地速度结构HVSR反演的适用性,为基于单个地震台的地震观测记录反演浅层速度结构提供了一种高效且较为准确的反演方法. 相似文献
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2D多尺度混合优化地球物理反演方法及其应用(英文) 总被引：1，自引：0，他引：1
局部优化和全局优化方法广泛应用到地球物理反演,但是两者各有其优缺点。将两类方法结合起来可以取长补短。将退火遗传算法（SAGA）和单纯形算法相结合,得到了一种高效、健全的2D非线性混合地震走时反演方法。首先,利用SAGA进行大范围的全局搜索,然后由单纯形方法进行快速局部搜索。为了降低层析成像的多解性,我们采用了多尺度逐次逼近的技巧。把速度场划分为不同的空间尺度,定义网格节点上的速度作为待反演参数,采用双三次样条函数模型参数化,正问题采用有限差分走时计算方法,反问题采用多尺度混合反演方法。一个低速度异常体的数值模拟试验和抗走时扰动试验表明该方法是有效和健全的。我们将该方法应用到青藏高原东北缘阿尼玛卿rlet,Meyer,Marr,缝合带东段上部地壳速度结构研究中。数字模型试验和实际资料的应用表明了方法的有效性和健全性。 相似文献
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反应谱的标定是抗震设计的基础工作之一,模拟退火算法是基于模拟固体退火过程而提出的多参数优化组合方法。本文提出将模拟退火算法应用于设计反应谱的标定,并概述了模拟退火算法的基本原理及特点,介绍了运用MATLAB基于模拟退火算法对反应谱进行标定的过程。检验了将模拟退火算法应用于场地相关谱标定的可行性和合理性,给出了将其应用于工程场地地震安全性评价中设计反应谱标定实例。通过检验和实例分析可以看出,基于模拟退火算法的反应谱标定方法所给出的设计反应谱谱形真实地反映了原地震反应谱的特征,较客观的反映了场地相关反应谱的峰值和周期特征。 相似文献
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本文将面向目标的自适应算法应用于三维大地电磁数值模拟.使用基于非结构网格的矢量有限单元法对起伏地表大地电磁正演模拟问题进行求解.使用利用垂向电流密度在物性界面上的连续性对后验误差进行估算的算法指导网格优化.由于全局自适应算法针对观测点优化网格的能力较差,本文通过求解正演问题的对偶问题计算后验误差的加权系数,并对相关加权系数进行改进,从而实现了面向目标的自适应算法.与传统基于结构化网格的电磁正演算法相比,采用非结构网格能够更好地拟合起伏地表和地下不规则异常体.由于使用了面向目标的自适应算法,本文能够使用更少的网格达到较高的计算精度.通过对比本文模拟结果与半空间响应和全局自适应算法计算结果,并通过对比使用改进前和改进后加权系数得到的网格剖分结果验证了本文算法的有效性. 相似文献
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Use of VFSA for resolution, sensitivity and uncertainty analysis in 1D DC resistivity and IP inversion 总被引：1，自引：0，他引：1
We present results from the resolution and sensitivity analysis of 1D DC resistivity and IP sounding data using a non-linear inversion. The inversion scheme uses a theoretically correct Metropolis–Gibbs' sampling technique and an approximate method using numerous models sampled by a global optimization algorithm called very fast simulated annealing (VFSA). VFSA has recently been found to be computationally efficient in several geophysical parameter estimation problems. Unlike conventional simulated annealing (SA), in VFSA the perturbations are generated from the model parameters according to a Cauchy-like distribution whose shape changes with each iteration. This results in an algorithm that converges much faster than a standard SA. In the course of finding the optimal solution, VFSA samples several models from the search space. All these models can be used to obtain estimates of uncertainty in the derived solution. This method makes no assumptions about the shape of an a posteriori probability density function in the model space. Here, we carry out a VFSA-based sensitivity analysis with several synthetic and field sounding data sets for resistivity and IP. The resolution capability of the VFSA algorithm as seen from the sensitivity analysis is satisfactory. The interpretation of VES and IP sounding data by VFSA, incorporating resolution, sensitivity and uncertainty of layer parameters, would generally be more useful than the conventional best-fit techniques. 相似文献
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Enforcing smoothness and assessing uncertainty in non-linear one-dimensional prestack seismic inversion 总被引：1，自引：0，他引：1
Estimation of elastic properties of rock formations from surface seismic amplitude measurements remains a subject of interest for the exploration and development of hydrocarbon reservoirs. This paper develops a global inversion technique to estimate and appraise 1D distributions of compressional‐wave velocity, shear‐wave velocity and bulk density, from normal‐moveout‐corrected PP prestack surface seismic amplitude measurements. Specific objectives are: (a) to evaluate the efficiency of the minimization algorithm (b) to appraise the impact of various data misfit functions, and (c) to assess the effect of the degree and type of smoothness criterion enforced by the inversion. Numerical experiments show that very fast simulated annealing is the most efficient minimization technique among alternative approaches considered for global inversion. It is also found that an adequate choice of data misfit function is necessary for a reliable and efficient match of noisy and sparse seismic amplitude measurements. Several procedures are considered to enforce smoothness of the estimated 1D distributions of elastic parameters, including predefined quadratic measures of length, flatness and roughness. Based on the general analysis of global inversion techniques, we introduce a new stochastic inversion algorithm that initializes the search for the minimum with constrained random distributions of elastic parameters and enforces predefined autocorrelation functions (semivariograms). This strategy readily lends itself to the assessment of model uncertainty. The new global inversion algorithm is successfully tested on noisy synthetic amplitude data. Moreover, we present a feasibility analysis of the resolution and uncertainty of prestack seismic amplitude data to infer 1D distributions of elastic parameters measured with wireline logs in the deepwater Gulf of Mexico. The new global inversion algorithm is computationally more efficient than the alternative global inversion procedures considered here. 相似文献
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本文实现了一种面向目标自适应海洋可控源电磁三维矢量有限元方法.为满足三维复杂电性结构模拟的需求,网格剖分采用非结构化六面体.在组装刚度矩阵之后,形成的大型复数线性方程组分解为等价的实数形式,利用带预条件的广义最小残差法进行求解.在获得微分方程的解之后,为提高解的准确性,通过面向目标的自适应误差估计来指示网格细化,重点加密能使观测点数值模拟精度提高的网格.对于大规模三维数据,为了使模型空间的并行计算达到均衡负载的效果,我们使用METIS函数库来进行网格计算任务量的划分.最后,通过对比一维解析解与三维自适应矢量有限元计算结果,验证了程序的正确性;通过自适应过程中误差指示子的分布,验证了面向目标自适应的有效性;通过对三维复杂模型进行均衡负载下的并行计算,测试了程序的可扩展性. 相似文献
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宽范围物性约束技术容易实现、具有一定容错性,目前已在大地电磁测深（MT）和地震、MT和重力联合反演中实现,但该技术是结合模拟退火算法实现的.差分进化算法（DE）是一种全局优化算法,但该算法在地球物理联合反演领域应用较少.基于此,本文以双种群设置方案为框架改进了DE算法,并提出了基于改进DE算法的宽范围物性约束技术.MT和重力联合反演的模型试验表明：与传统的DE算法相比,改进的DE算法收敛速度更快,寻优能力更强;基于改进DE算法的宽范围物性约束技术可以促进不同岩石物性参数在一定"范围"内实现耦合,既可以利用岩石物性关联的导向作用,又可以发挥优化算法的寻优能力,进而降低地球物理联合反演对先验信息的要求;此外,该技术的实现也验证了宽范围物性约束思想在联合反演领域中的适用性,具有进一步推广至其他优化算法中的潜质. 相似文献
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振幅随偏移距变化（AVO）反演是一个非线性的组合最优化过程,理论上可先将该非线性问题线性化,然后求解线性问题;或者直接利用非线性的模拟退火、遗传算法等方法求解.但无论哪种反演思路,实际中影响其精度的因素很多,因此分析AVO反演中的误差来源对提高反演精度和评价反演方法的可靠性非常重要.本文对能造成反演误差的主要因素,噪音、薄层调谐、地震数据处理中的误差、入射角范围等进行了分析,讨论了这些因素可能对反演结果造成的影响,发现在AVO反演过程中可以从优化参数选择和针对性处理方面来减小这些误差,提高反演精度. 相似文献
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In this paper, a global inversion method is developed for seismic moment tensor inversion by using the body wave forms. The
algorithm depends on neither the selection of starting model nor the forms of objective function and constraints. When the
error function, measure of the difference between the observed and synthetic waveforms, is chosen as the objective function,
the best fitting source model is found; when a certain combination in seismic moment tensor elements is selected as the objective
function and the values of error function are constrained in a suitable bound, the extreme source models can be produced by
minimizing or maximizing this combination. By changing the form of the combination of moment tensor elements, a variety of
different source characteristics can be considered. Therefore the extreme solution provides an estimation of the uncertainty
in the best fitting source model. The seismic waveform data was used to evaluate the effectiveness of this algorithm.
This research was supported by the National Natural Science Foundation of China. 相似文献