共查询到20条相似文献，搜索用时 46 毫秒
1.
Shiuan Wan Shih-Hsun Chang Ching-Tung Peng Yu-Keng Chen 《Arabian Journal of Geosciences》2017,10(9):215
Rice is a crop of global importance. To predict the area of paddy rice and thus its production, it draws great attraction of using data mining approaches on remote sensing data, which are well accepted. Many approaches based on supervised and unsupervised learning techniques have been developed over the years. Artificial bee colony (ABC) algorithm with a clustering technique is one of the most popular swarm-based algorithms. In this study, ABC algorithm is used to perform the rice image classification based on remote sensing imagery. This study comprises two stages. In the first part of the study, the ancillary information composed from the original spectra is applied to increase the performance of classification. As the other parts of the study, an efficient unsupervised classifier is developed to evaluate the performance of the incorporated ancillary information. This study integrates the ABC algorithm into a clustering process to build a land cover classifier system. On the other hand, a parallel approach using ant colony optimization (ACO) is studied for comparison. Two significant contributions are presented in this study: (1) a paddy rice image classifier is built with ABC algorithm and (2) the outcome of classifier using ABC algorithm outperforms that using ACO algorithm. 相似文献
2.
M. J. Alizadeh A. Shabani M. R. Kavianpour 《International Journal of Environmental Science and Technology》2017,14(11):2399-2410
A reliable prediction of dispersion coefficient can provide valuable information for environmental scientists and river engineers as well. The main objective of this study is to apply intelligence techniques for predicting longitudinal dispersion coefficient in rivers. In this regard, artificial neural network (ANN) models were developed. Four different metaheuristic algorithms including genetic algorithm (GA), imperialist competitive algorithm (ICA), bee algorithm (BA) and cuckoo search (CS) algorithm were employed to train the ANN models. The results obtained through the optimization algorithms were compared with the Levenberg–Marquardt (LM) algorithm (conventional algorithm for training ANN). Overall, a relatively high correlation between measured and predicted values of dispersion coefficient was observed when the ANN models trained with the optimization algorithms. This study demonstrates that the metaheuristic algorithms can be successfully applied to make an improvement on the performance of the conventional ANN models. Also, the CS, ICA and BA algorithms remarkably outperform the GA and LM algorithms to train the ANN model. The results show superiority of the performance of the proposed model over the previous equations in terms of DR, R 2 and RMSE. 相似文献
3.
In this study, we introduce the application of data mining to petroleum exploration and development to obtain high-performance predictive models and optimal classifications of geology, reservoirs, reservoir beds, and fluid properties. Data mining is a practical method for finding characteristics of, and inherent laws in massive multi-dimensional data. The data mining method is primarily composed of three loops, which are feature selection, model parameter optimization, and model performance evaluation. The method’s key techniques involve applying genetic algorithms to carry out feature selection and parameter optimization and using repeated cross-validation methods to obtain unbiased estimation of generalization accuracy. The optimal model is finally selected from the various algorithms tested. In this paper, the evaluation of water-flooded layers and the classification of conglomerate reservoirs in Karamay oil field are selected as case studies to analyze comprehensively two important functions in data mining, namely predictive modeling and cluster analysis. For the evaluation of water-flooded layers, six feature subset schemes and five distinct types of data mining methods (decision trees, artificial neural networks, support vector machines, Bayesian networks, and ensemble learning) are analyzed and compared. The results clearly demonstrate that decision trees are superior to the other methods in terms of predictive model accuracy and interpretability. Therefore, a decision tree-based model is selected as the final model for identifying water-flooded layers in the conglomerate reservoir. For the reservoir classification, the reservoir classification standards from four types of clustering algorithms, such as those based on division, level, model, and density, are comparatively analyzed. The results clearly indicate that the clustering derived from applying the standard K-means algorithm, which is based on division, provides the best fit to the geological characteristics of the actual reservoir and the greatest accuracy of reservoir classification. Moreover, the internal measurement parameters of this algorithm, such as compactness, efficiency, and resolution, are all better than those of the other three algorithms. Compared with traditional methods from exploration geophysics, the data mining method has obvious advantages in solving problems involving calculation of reservoir parameters and reservoir classification using different specialized field data. Hence, the effective application of data mining methods can provide better services for petroleum exploration and development. 相似文献
4.
Well placement optimization with the covariance matrix adaptation evolution strategy and meta-models
The amount of hydrocarbon recovered can be considerably increased by finding optimal placement of non-conventional wells.
For that purpose, the use of optimization algorithms, where the objective function is evaluated using a reservoir simulator,
is needed. Furthermore, for complex reservoir geologies with high heterogeneities, the optimization problem requires algorithms
able to cope with the non-regularity of the objective function. In this paper, we propose an optimization methodology for
determining optimal well locations and trajectories based on the covariance matrix adaptation evolution strategy (CMA-ES)
which is recognized as one of the most powerful derivative-free optimizers for continuous optimization. In addition, to improve
the optimization procedure, two new techniques are proposed: (a) adaptive penalization with rejection in order to handle well
placement constraints and (b) incorporation of a meta-model, based on locally weighted regression, into CMA-ES, using an approximate
stochastic ranking procedure, in order to reduce the number of reservoir simulations required to evaluate the objective function.
The approach is applied to the PUNQ-S3 case and compared with a genetic algorithm (GA) incorporating the Genocop III technique
for handling constraints. To allow a fair comparison, both algorithms are used without parameter tuning on the problem, and
standard settings are used for the GA and default settings for CMA-ES. It is shown that our new approach outperforms the genetic
algorithm: It leads in general to both a higher net present value and a significant reduction in the number of reservoir simulations
needed to reach a good well configuration. Moreover, coupling CMA-ES with a meta-model leads to further improvement, which
was around 20% for the synthetic case in this study. 相似文献
5.
In this paper, an enhanced backtracking search algorithm (so-called MBSA-LS) for parameter identification is proposed with two modifications: (a) modifying the mutation of original backtracking search algorithm (BSA) considering the contribution of current best individual for accelerating convergence speed and (b) novelly incorporating an efficient differential evolution (DE) as local search for improving the quality of population. The proposed MBSA-LS is first validated with better performance than the original BSA and some other typical state-of-the-art optimization algorithms on a benchmark of soil parameter identification in terms of effectiveness, efficiency, and robustness. Then, the efficiency of the MBSA-LS is further illustrated by two representative cases: identifying soil parameters from both laboratory tests and field measurements. All comparisons demonstrate that the proposed MBSA-LS algorithm can give accurate results in a short time. Finally, to conveniently solve the problems of parameter identification, a practical tool ErosOpt for parameter identification is developed by integrating the proposed MBSA-LS and some other efficient algorithms for readers to conduct the parameter identification using optimisation algorithms. 相似文献
6.
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
7.
Iterative methods for the solution of non‐linear finite element equations are generally based on variants of the Newton–Raphson method. When they are stable, full Newton–Raphson schemes usually converge rapidly but may be expensive for some types of problems (for example, when the tangent stiffness matrix is unsymmetric). Initial stiffness schemes, on the other hand, are extremely robust but may require large numbers of iterations for cases where the plastic zone is extensive. In most geomechanics applications it is generally preferable to use a tangent stiffness scheme, but there are situations in which initial stiffness schemes are very useful. These situations include problems where a nonassociated flow rule is used or where the zone of plastic yielding is highly localized. This paper surveys the performance of several single‐parameter techniques for accelerating the convergence of the initial stiffness scheme. Some simple but effective modifications to these procedures are also proposed. In particular, a modified version of Thomas' acceleration scheme is developed which has a good rate of convergence. Previously published results on the performance of various acceleration algorithms for initial stiffness iteration are rare and have been restricted to relatively simple yield criteria and simple problems. In this study, detailed numerical results are presented for the expansion of a thick cylinder, the collapse of a rigid strip footing, and the failure of a vertical cut. These analyses use the Mohr–Coulomb and Tresca yield criteria which are popular in soil mechanics. Copyright © 2000 John Wiley & Sons, Ltd. 相似文献
8.
Qiang Guo Hongbing Zhang Jingbo Tian Lifeng Liang Zuoping Shang 《Arabian Journal of Geosciences》2018,11(3):48
Multiparameter prestack seismic inversion is one of the most powerful techniques in quantitatively estimating subsurface petrophysical properties. However, it remains a challenging problem due to the nonlinearity and ill-posedness of the inversion process. Traditional regularization approach can stabilize the solution but at the cost of smoothing valuable geological boundaries. In addition, compared with linearized optimization methods, global optimization techniques can obtain better results regardless of initial models, especially for multiparameter prestack inversion. However, when solving multiparameter prestack inversion problems, the application of standard global optimization algorithms maybe limited due to the issue of high computational cost (e.g., simulating annealing) or premature convergence (e.g., particle swarm optimization). In this paper, we propose a hybrid optimization-based multiparameter prestack inversion method. In this method, we introduce a prior constraint term featured by multiple regularization functions, intended to preserve layered boundaries of geological formations; in particular, to address the problem of premature convergence existing in standard particle swarm optimization algorithm, we propose a hybrid optimization strategy by hybridizing particle swarm optimization and very fast simulating annealing to solve the nonlinear optimization problem. We demonstrate the effectiveness of the proposed inversion method by conducting synthetic test and field data application, both of which show encouraging results. 相似文献
9.
针对传统基于单一目标的水文模型参数优化率定方法不能充分挖掘水文系统不同动态行为特征的缺陷,提出一种多目标文化混合复形差分进化算法(Multi-objective Culture Shuffled Complex Differential Evolution,MOCSCDE)用于求解水文模型参数多目标优化问题。MOCSCDE算法将混合复形进化算法(Shuffled Complex Evolution,SCE-UA)置于文化算法(Cultural Algorithms,CA)进化的框架中,利用种群进化过程中提取的各种知识指导算法的运行,提高算法的运行效率,同时考虑到SCE-UA中单纯形算子不能充分利用种群个体信息的不足,采用全局搜索能力强的差分进化算法(Differential Evolution,DE)替代单纯形算子,可以更加充分利用种群个体信息进行演化计算,进一步提高算法的计算效率。将MOCSCDE算法应用于概念性水文模型——新安江模型的参数多目标优化率定,并与NSGA-Ⅱ和SPEA2算法进行对比分析,结果表明MOCSCDE算法的收敛性和分布性均优于NSGA-Ⅱ和SPEA2,可为水文预报提供更为全面可靠的参数组合决策依据。 相似文献
10.
人工神经网络具有很强的非线性处理能力,能够有效地模拟复杂的非线性径流预报过程。传统的基于BP训练算法的人工神经网络具有训练时间较长,容易陷于局部最优值等缺陷,本文对训练算法加以改进,分别使用平均线性粒子群,粒子群和BP算法来优化人工神经网络的各项参数,首先使用标准函数测试了3种算法的全局优化性能,然后用它们对三峡水库的入库径流进行预报,以比较它们的预报性能。结果表明,在3种算法中,平均线性粒子群算法全局寻优的速度最快,稳定性最高,基于平均线性粒子群算法的人工神经网络的径流预报的精度也最高。 相似文献
11.
In most research studies, the problem of locating additional drillhole is simplified, and the ore body is considered as a 2d object. In this study, location of additional drillholes are optimized by considering the third dimension of the ore body, the azimuth and the dip of additional drill holes. A new objective function is defined to address the effect of rock type in locating new drillholes. The optimization problem is solved using a novel fuzzy-artificial bee colony algorithm, called FABC. The parameters of the FABC algorithm is dynamically adjusted using a designed fuzzy inference system with three performance measures as inputs and two outputs. The comparison performance with state-of-the-art optimization algorithm, using a nonparametric hypothesis test, indicates higher performance of the FABC algorithm. The results indicate significantly a decrease of kriging variance by introducing additional drillholes. 相似文献
12.
基于遗传算法的新安江模型日模拟参数优选研究 总被引：7，自引：0，他引：7
在概念性水文模型的参数率定中,目前还没有一个传统优化方法能够提供保证足够高效和稳定性的算法。为了克服传统优化方法中局部收敛性的缺点,近年来利用遗传算法通过计算机准确稳定地进行概念性水文模型的参数优选的尝试得到越来越多的重视和发展。目前优选水文模型待定参数,大多是从次洪模型的方面去讨论,有关日模拟模型的遗传算法参数优选讨论的较少。本文系统分析了基于遗传算法的新安江模型日模拟参数的自动优选,同时针对遗传算法在模型参数众多的情况下时间效率低下问题,通过利用新安江模型参数分层原理与模型参数敏感性分析对优选结果影响,提出一套简化的日模型参数遗传算法优选方案。经过流域模拟检验,该优选方案可行,运行效率高,可以作为类似模型遗传算法参数率定快速、有效的方案。 相似文献
13.
There is no gainsaying that determining the optimal number, type, and location of hydrocarbon reservoir wells is a very important aspect of field development planning. The reason behind this fact is not farfetched—the objective of any field development exercise is to maximize the total hydrocarbon recovery, which for all intents and purposes, can be measured by an economic criterion such as the net present value of the reservoir during its estimated operational life-cycle. Since the cost of drilling and completion of wells can be significantly high (millions of dollars), there is need for some form of operational and economic justification of potential well configuration, so that the ultimate purpose of maximizing production and asset value is not defeated in the long run. The problem, however, is that well optimization problems are by no means trivial. Inherent drawbacks include the associated computational cost of evaluating the objective function, the high dimensionality of the search space, and the effects of a continuous range of geological uncertainty. In this paper, the differential evolution (DE) and the particle swarm optimization (PSO) algorithms are applied to well placement problems. The results emanating from both algorithms are compared with results obtained by applying a third algorithm called hybrid particle swarm differential evolution (HPSDE)—a product of the hybridization of DE and PSO algorithms. Three cases involving the placement of vertical wells in 2-D and 3-D reservoir models are considered. In two of the three cases, a max-mean objective robust optimization was performed to address geological uncertainty arising from the mismatch between real physical reservoir and the reservoir model. We demonstrate that the performance of DE and PSO algorithms is dependent on the total number of function evaluations performed; importantly, we show that in all cases, HPSDE algorithm outperforms both DE and PSO algorithms. Based on the evidence of these findings, we hold the view that hybridized metaheuristic optimization algorithms (such as HPSDE) are applicable in this problem domain and could be potentially useful in other reservoir engineering problems. 相似文献
14.
水文频率分析在参数估计过程中常采用智能优化适线法,如蚁群算法、遗传算法、粒子群算法、模拟退火算法等,但这些算法模型参数难以有效确定,导致寻优结果存在不稳定的不足。为了克服传统优化适线法的缺陷,在系统阐述群居蜘蛛优化算法基本原理的基础上,将群居蜘蛛优化算法用于水文频率曲线的参数确定中,并与传统的参数估计方法(矩法、权函数法、概率权重矩法、遗传算法)加以比较。实例结果表明,该方法搜索效率高,寻优结果稳定,能较好获得参数的最优解。 相似文献
15.
采用三峡水库汛期分期方案,将蓄水时间提前至汛末期,综合考虑上下游防洪、发电、通航和蓄满率等要求,建立了多目标蓄水调度模型,构建了"优化-模拟-检验"的算法流程,采用遗传算法进行求解。结果表明,最优方案在满足上下游防洪安全要求的前提下,蓄水期可增发电量17%,减少弃水44%,蓄满率和通航保证率显著提高。通过对汛末期防洪库容进行科学划分和对蓄水调度图进行优化,既确保防洪安全又最大限度挖掘兴利效益,为研究水库汛末蓄水调度问题提供了一种新的思路和方法。 相似文献
16.
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
17.
Sylvain Weill Raphael di Chiara-Roupert Philippe Ackerer 《Computational Geosciences》2014,18(5):697-709
The diffusive wave approximation of the Saint-Venant equations is commonly used in hydrological models to describe surface flow processes. Numerous numerical approaches can be used to solve this highly nonlinear equation. Nonlinear time integration schemes—also called methods of lines (MOL)—were proven very efficient to solve other nonlinear problems in geosciences but were never considered to deal with surface flow modeling with the diffusive wave equation. In this paper, we study the relative performance of different time and space integration schemes by comparing the results obtained with classical approaches and with nonlinear time integration approaches. The results show that (i) the integration method with a higher order in space shows high accuracy regarding an integrated indicator such as the global mass balance error but is less accurate regarding local indicators, and (ii) nonlinear time integration techniques perform better than classical ones. Overall, it seems that integration techniques combining nonlinear time integration and a low spatial order need to be considered when developing hydrological modeling tools owing to their simplicity of implementation and very good performance. 相似文献
18.
Mohammad Ethteram Sayed-Farhad Mousavi Hojat Karami Saeed Farzin Ravinesh Deo Faridah Binti Othman Kwok-wing Chau Saeed Sarkamaryan Vijay P. Singh Ahmed El-Shafie 《Environmental Earth Sciences》2018,77(13):510
Optimizing reservoir operation rule is considered as a complex engineering problem which requires an efficient algorithm to solve. During the past decade, several optimization algorithms have been applied to solve complex engineering problems, which water resource decision-makers can employ to optimize reservoir operation. This study investigates one of the new optimization algorithms, namely, Bat Algorithm (BA). The BA is incorporated with different rule curves, including first-, second-, and third-order rule curves. Two case studies, Aydoughmoush dam and Karoun 4 dam in Iran, are considered to evaluate the performance of the algorithm. The main purpose of the Aydoughmoush dam is to supply water for irrigation. Hence, the objective function for the optimization model is to minimize irrigation deficit. On the other hand, Karoun 4 dam is designed for hydropower generation. Three different evaluation indices, namely, reliability, resilience, and vulnerability were considered to examine the performance of the algorithm. Results showed that the bat algorithm with third-order rule curve converged to the minimum objective function for both case studies and achieved the highest values of reliability index and resiliency index and the lowest value of the vulnerability index. Hence, the bat algorithm with third-order rule curve can be considered as an appropriate optimization model for reservoir operation. 相似文献
19.
Ayman Fazea Al-Lehyani Abdullatif Abdulrahman Al-Shuhail 《Arabian Journal of Geosciences》2014,7(2):757-771
Near-surface diffractors are one of the problems in land seismic exploration. They can scatter the surface wave energy emanating from the seismic source and contaminate the signal received by seismic receivers. The scattered energy from the near-surface diffractors manifests itself on seismic shot gathers as strong hyperbolic events, called diffractions, masking the weakly reflected body waves. Diffractions present complications to most of surface-wave suppression schemes, especially when they have been scattered by scatterers outside the receiver line. Different methods have been used to eliminate diffractions from seismic data, including geophone arrays, filtering, and inverse scattering. Each of those methods has its own limitations. In this study, we present processing algorithms to map and attenuate near-surface diffractors of surface waves in seismic shot gathers. The mapping algorithm is based on semblance measurements and time–offset relations, while the attenuation algorithm is based on the least-square fitting of a source wavelet. The algorithms are applied on synthetic data from two different models. The first model has three near-surface diffractors, while the second model has three clusters of near-surface diffractors. Each cluster consists of three near-surface diffractors with a different geometry for each cluster. The results show that the proposed algorithms are successful in locating and attenuating most near-surface diffractors, except when the separation between individual diffractors is below the wavelength of the diffracted surface wave. 相似文献
20.
The present paper introduces a genetic algorithm-based optimization technique to calibrate a nonlinear strain hardening–softening constitutive model for soils using five material parameters. The efficiency of the proposed technique is analyzed through the use of different GA techniques. The effects of elitism, crossover, and mutation, as well as population size, on the performance of the conventional GAs for this problem are investigated. Micro-genetic algorithms (mGAs) are chosen and tested for different population sizes. The mGAs with a population size of five yields the optimal parameter values after fewer function evaluations and capture the overall simulated or experimental behavior at every point in stress–strain and strain paths in triaxial compression. The proposed calibration technique is validated through comparison with the traditional calibration technique. 相似文献