共查询到20条相似文献，搜索用时 31 毫秒
1.
Simulating natural ants’ foraging behavior, the ant colony optimization (ACO) algorithm performs excellently in combinational optimization problems, for example the traveling salesman problem and the quadratic assignment problem. However, the ACO is seldom used to inverted for gravitational and magnetic data. On the basis of the continuous and multi-dimensional objective function for potential field data optimization inversion, we present the node partition strategy ACO (NP-ACO) algorithm for inversion of model variables of fixed shape and recovery of physical property distributions of complicated shape models. We divide the continuous variables into discrete nodes and ants directionally tour the nodes by use of transition probabilities. We update the pheromone trails by use of Gaussian mapping between the objective function value and the quantity of pheromone. It can analyze the search results in real time and promote the rate of convergence and precision of inversion. Traditional mapping, including the ant-cycle system, weaken the differences between ant individuals and lead to premature convergence. We tested our method by use of synthetic data and real data from scenarios involving gravity and magnetic anomalies. The inverted model variables and recovered physical property distributions were in good agreement with the true values. The ACO algorithm for binary representation imaging and full imaging can recover sharper physical property distributions than traditional linear inversion methods. The ACO has good optimization capability and some excellent characteristics, for example robustness, parallel implementation, and portability, compared with other stochastic metaheuristics. 相似文献
2.
Pumping optimization of coastal aquifers based on evolutionary algorithms and surrogate modular neural network models 总被引：4，自引：0，他引：4
Pumping optimization of coastal aquifers involves complex numerical models. In problems with many decision variables, the computational burden for reaching the optimal solution can be excessive. Artificial Neural Networks (ANN) are flexible function approximators and have been used as surrogate models of complex numerical models in groundwater optimization. However, this approach is not practical in cases where the number of decision variables is large, because the required neural network structure can be very complex and difficult to train. The present study develops an optimization method based on modular neural networks, in which several small subnetwork modules, trained using a fast adaptive procedure, cooperate to solve a complex pumping optimization problem with many decision variables. The method utilizes the fact that salinity distribution in the aquifer, depends more on pumping from nearby wells rather than from distant ones. Each subnetwork predicts salinity in only one monitoring well, and is controlled by relatively few pumping wells falling within certain control distance from the monitoring well. While the initial control area is radial, its shape is adaptively improved using a Hermite interpolation procedure. The modular neural subnetworks are trained adaptively during optimization, and it is possible to retrain only the ones not performing well. As optimization progresses, the subnetworks are adapted to maximize performance near the current search space of the optimization algorithm. The modular neural subnetwork models are combined with an efficient optimization algorithm and are applied to a real coastal aquifer in the Greek island of Santorini. The numerical code SEAWAT was selected for solving the partial differential equations of flow and density dependent transport. The decision variables correspond to pumping rates from 34 wells. The modular subnetwork implementation resulted in significant reduction in CPU time and identified an even better solution than the original numerical model. 相似文献
3.
Yoshihiro Kanno 《地震工程与结构动力学》2013,42(11):1657-1676
Supplemental damping is known as an efficient and practical means to improve seismic response of building structures. Presented in this paper is a mixed‐integer programming approach to find the optimal placement of supplemental dampers in a given shear building model. The damping coefficients of dampers are treated as discrete design variables. It is shown that a minimization problem of the sum of the transfer function amplitudes of the interstory drifts can be formulated as a mixed‐integer second‐order cone programming problem. The global optimal solution of the optimization problem is then found by using a solver based on a branch‐and‐cut algorithm. Two numerical examples in literature are solved with discrete design variables. In one of these examples, the proposed method finds a better solution than an existing method in literature developed for the continuous optimal damper placement problem. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
4.
《Advances in water resources》2007,30(4):954-965
This paper exploits the unique feature of the Ant Colony Optimization Algorithm (ACOA), namely incremental solution building mechanism, to develop partially constraint ACO algorithms for the solution of optimization problems with explicit constraints. The method is based on the provision of a tabu list for each ant at each decision point of the problem so that some constraints of the problem are satisfied. The application of the method to the problem of storm water network design is formulated and presented. The network nodes are considered as the decision points and the nodal elevations of the network are used as the decision variables of the optimization problem. Two partially constrained ACO algorithms are formulated and applied to a benchmark example of storm water network design and the results are compared with those of the original unconstrained algorithm and existing methods. In the first algorithm the positive slope constraints are satisfied explicitly and the rest are satisfied by using the penalty method while in the second one the satisfaction of constraints regarding the maximum ratio of flow depth to the diameter are also achieved explicitly via the tabu list. The method is shown to be very effective and efficient in locating the optimal solutions and in terms of the convergence characteristics of the resulting ACO algorithms. The proposed algorithms are also shown to be relatively insensitive to the initial colony used compared to the original algorithm. Furthermore, the method proves itself capable of finding an optimal or near-optimal solution, independent of the discretisation level and the size of the colony used. 相似文献
5.
当前对建筑空间结构进行优化时,所采用的算法趋同性高,无法实现多目标种群优化,易陷入局部最优解,存在寻优质量低、优化成本高、抗震性能低的问题。针对上述问题,提出一种基于改进粒子群算法的建筑空间结构优化方法。该方法以空间结构的抗震性能、工程造价为优化目标,来优化建立建筑空间结构设计;引入多子群协同进化机制解决建筑空间结构抗震优化设计中多目标间的种群优化问题,同时引入外部档案和精英学习策略改进粒子群算法,筛选出满足目标函数的最优设计方案,完成抗震性约束的建筑空间结构优化。实验结果表明:所提方法对建筑空间结构优化时的特点为寻优质量高、优化成本低、抗震性能高。 相似文献
6.
This study demonstrates the utilization of a multi-objective hybrid global/local optimization algorithm for solving managed aquifer recharge (MAR) design problems, in which the decision variables included spatial arrangement of water injection and abstraction wells and time-variant rates of pumping and injection. The objective of the optimization was to maximize the efficiency of the MAR scheme, which includes both quantitative and qualitative aspects. The case study used to demonstrate the capabilities of the proposed approach is based on a published report on designing a real MAR site with defined aquifer properties, chemical groundwater characteristics as well as quality and volumes of injected water. The demonstration problems include steady state and transient scenarios. The steady state scenario demonstrates optimization of spatial arrangement of multiple injection and recovery wells, whereas the transient scenario was developed with the purpose of finding optimal regimes of water injection and recovery at a single location. Both problems were defined as multi-objective problems. The scenarios were simulated by applying coupled numerical groundwater flow and solute transport models: MODFLOW-2005 and MT3D-USGS. The applied optimization method was a combination of global (the non-dominated sorting genetic algorithm [NSGA-2]) and local (the Nelder-Mead downhill simplex search algorithms). The analysis of the resulting Pareto optimal solutions led to the discovery of valuable patterns and dependencies between the decision variables, model properties, and problem objectives. Additionally, the performance of the traditional global and the hybrid optimization schemes were compared. 相似文献
7.
A new variational method is proposed for grid generation in numerical solution of groundwater flow problems requiring imposition of constraints on the space step. The proposed procedure utilizes the penalty function method for the solution of an optimization problem with inequality constraints. The efficiency of the method is illustrated by the generation of solution-dependent grids, which allow such constraints to be satisfied for complex-geometry domains. 相似文献
8.
A new approximate method of solution for stochastic optimal control problems with many state and control variables is introduced. The method is based on the expansion of the optimal control into the deterministic feedback control plus a caution term. The analytic, small-perturbation calculation of the caution term is at the heart of the new method. The developed approximation depends only on the first two statistical moments of the random inputs and up to the third derivatives of the cost functions. Its computational requirements do not exhibit the exponential growth exhibited by discrete stochastic DP and can be used as a suboptimal solution to problems for which application of stochastic DP is not feasible. The method is accurate when the cost-to-go functions are approximately cubic in a neighbourhood around the deterministic trajectory whose size depends on forecasting uncertainty. Furthermore, the method elucidates the stochastic optimization problem yielding insights which cannot be easily obtained from the numerical application of discrete DP. 相似文献
9.
《Advances in water resources》2001,24(8):827-841
Although models are now routinely used for addressing environmental problems, both in research and management applications, the problem of obtaining the required parameters remains a major challenge. An attractive procedure for obtaining model parameters in recent years has been through inverse modeling. This approach involves obtaining easily measurable variables (model output), and using this information to estimate a set of unknown model parameters. Inverse procedures usually require optimization of an objective function. In this study we emulate the behavior of a colony of ants to achieve this optimization. The method uses the fact that ants are capable of finding the shortest path from a food source to their nest by depositing a trail of pheromone during their walk. Results obtained with the ant colony parameter optimization method are very promising; in eight different applications we were able to estimate the `true' parameters to within a few percent. One such study is reported in this paper plus an application to estimating hydraulic parameters in a lysimeter experiment. Despite the encouraging results obtained thus far, further improvements could still be made in the parameterization of the ant colony optimization for application to estimation of unsaturated flow and transport parameters. 相似文献
10.
In rainfall–runoff studies, it is often necessary to change the duration of a given unit hydrograph. Nash's Instantaneous Unit Hydrograph (IUH) is an ideal method that eliminates the hydrograph duration. This paper presents the results of the application of search algorithms, namely a genetic algorithm and hill climbing, to develop the IUH that minimizes the error between the observed and generated hydrographs. Also the performance of these methods has been compared with that of the classical method used for estimation of IUH, namely the method of moments. The genetic algorithm is a popular search procedure for function optimization that applies the mechanics of natural genetics and natural selection to explore a given search space. Hill climbing is an optimization technique that belongs to the family of local search and algorithms can be used to solve problems that have many solutions, with some solutions better than others. The results obtained from both the genetic algorithm and hill climbing algorithm for estimation of Nash's IUH parameters were compared with the results obtained by the method of moments for storms from two river basins that are located in different climatic regions. It was found that both the genetic algorithm and hill climbing provided improved and consistent results. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
11.
Alon Ziv 《Journal of Seismology》2016,20(4):1123-1129
A major difficulty in inverting geodetic data for fault slip distribution is that measurement errors are mapped from the data space onto the solution space. The amplitude of this mapping is sensitive to the condition number of the inverse problem, i.e., the ratio between the largest and smallest singular value of the forward matrix. Thus, unless the problem is well-conditioned, slip inversions cannot reveal the actual fault slip distribution. In this study, we describe a new iterative algorithm that optimizes the condition of the slip inversion through discretization of InSAR data. We present a numerical example that demonstrates the effectiveness of our approach. We show that the condition number of the reconditioned data sets are not only much smaller than those of uniformly spaced data sets with the same dimension but are also much smaller than non-uniformly spaced data sets, with data density that increases towards the model fault. 相似文献
12.
13.
Complexities in river discharge, variable rainfall regime, and drought severity merit the use of advanced optimization tools in multi-reservoir operation. The gravity search algorithm (GSA) is an evolutionary optimization algorithm based on the law of gravity and mass interactions. This paper explores the GSA's efficacy for solving benchmark functions, single reservoir, and four-reservoir operation optimization problems. The GSA's solutions are compared with those of the well-known genetic algorithm (GA) in three optimization problems. The results show that the GSA's results are closer to the optimal solutions than the GA's results in minimizing the benchmark functions. The average values of the objective function equal 1.218 and 1.746 with the GSA and GA, respectively, in solving the single-reservoir hydropower operation problem. The global solution equals 1.213 for this same problem. The GSA converged to 99.97% of the global solution in its average-performing history, while the GA converged to 97% of the global solution of the four-reservoir problem. Requiring fewer parameters for algorithmic implementation and reaching the optimal solution in fewer number of functional evaluations are additional advantages of the GSA over the GA. The results of the three optimization problems demonstrate a superior performance of the GSA for optimizing general mathematical problems and the operation of reservoir systems. 相似文献
14.
地球物理资料群体智能反演(英文) 总被引：6，自引：4，他引：2
复杂地球物理资料的反演问题往往是一个求解多参数非线性多极值的最优解问题。而鸟和蚂蚁等群体觅食的过程,正好与寻找地球物理反演最优解的过程相似。基于自然界群体协调寻优的思想,本文提出了交叉学科的群体智能地球物理资料反演方法,并给出了其对应的数学模型。用一个有无限多个局部最优解的已知模型对该类方法进行了试验。然后,将它们应用到了不同的复杂地球物理反演问题中:(1)对噪声敏感的线性问题;(2)非线性和线性同步反演问题;(3)非线性问题。反演结果表明,群体智能反演是可行的。与常规遗传算法和模拟退火法相比,该类方法有收敛速度相对快、收敛精度相对高等优点;与拟牛顿法和列文伯格一马夸特法相比,该类方法有能跳出局部最优解等优点。 相似文献
15.
The paper describes an optimization method for the solution of groundwater management problems. The method consists of a combination of the computation of horizontal plane groundwater flow with a free surface (finite element method) and a linear optimization procedure (simplex algorithm). Considering the special structure of data which result form computing the groundwater flow with the finite element method, and modifying the simplex algorithm, the solution of management problems with complex groundwater flow is realized without any difficulties. Compared to a flow computation alone the additional effort of the optimization (computer time and scope for data storage) is only small. 相似文献
16.
A. H. ELSheikh C. C. Pain F. Fang J. L. M. A. Gomes I. M. Navon 《Stochastic Environmental Research and Risk Assessment (SERRA)》2013,27(4):877-897
A new parameter estimation algorithm based on ensemble Kalman filter (EnKF) is developed. The developed algorithm combined with the proposed problem parametrization offers an efficient parameter estimation method that converges using very small ensembles. The inverse problem is formulated as a sequential data integration problem. Gaussian process regression is used to integrate the prior knowledge (static data). The search space is further parameterized using Karhunen–Loève expansion to build a set of basis functions that spans the search space. Optimal weights of the reduced basis functions are estimated by an iterative regularized EnKF algorithm. The filter is converted to an optimization algorithm by using a pseudo time-stepping technique such that the model output matches the time dependent data. The EnKF Kalman gain matrix is regularized using truncated SVD to filter out noisy correlations. Numerical results show that the proposed algorithm is a promising approach for parameter estimation of subsurface flow models. 相似文献
17.
We develop a new approach for solving the nonlinear Richards’ equation arising in variably saturated flow modeling. The growing complexity of geometric models for simulation of subsurface flows leads to the necessity of using unstructured meshes and advanced discretization methods. Typically, a numerical solution is obtained by first discretizing PDEs and then solving the resulting system of nonlinear discrete equations with a Newton-Raphson-type method. Efficiency and robustness of the existing solvers rely on many factors, including an empiric quality control of intermediate iterates, complexity of the employed discretization method and a customized preconditioner. We propose and analyze a new preconditioning strategy that is based on a stable discretization of the continuum Jacobian. We will show with numerical experiments for challenging problems in subsurface hydrology that this new preconditioner improves convergence of the existing Jacobian-free solvers 3-20 times. We also show that the Picard method with this preconditioner becomes a more efficient nonlinear solver than a few widely used Jacobian-free solvers. 相似文献
18.
本文提出一个较差分法精确的波动方程的数值解法--拟合法,拟合法与差分法的区别在于方程的离散化方法.文章通过对差分离散化实现过程的分析指出差分离散化存在的不足,并进而给出了拟合离散化方法.与差分法比较,拟合法的算子系数具有方程整体统筹性,是在具体的采样间隔、步长条件下的最佳系数,因而能适应样点间隔、步长等条件的变化,精度较高.文中还给出了模型及实例. 相似文献
19.
详细研究了一般地球物理反问题的迭代优化求解过程与物理学中原子跃迁过程的对应关系,建立了反演问题中模型空间、初始模型、局部极值模型、最优化模型等与原子的态空间、定态、激发态、基态等的对应关系. 在此基础上,模拟了物理学中原子从激发态向基态跃迁的物理过程,建立了一种与原子跃迁过程相对应的非线性随机跃迁数学模型和模型解跃迁搜索准则,导出了适用于一般地球物理资料的模拟原子跃迁的非线性反演算法. 用理论测试函数对这种新的反演方法进行了数值试验,结果表明该方法具有解不依赖于初始模型、收敛速度快等优点. 相似文献
20.
—?A recently developed direct search method for inversion, known as a neighbourhood algorithm (NA), is applied to the hypocentre location problem. Like some previous methods the algorithm uses randomised, or stochastic, sampling of a four-dimensional hypocentral parameter space, to search for solutions with acceptable data fit. Considerable flexibility is allowed in the choice of misfit measure.¶At each stage the hypocentral parameter space is partitioned into a series of convex polygons called Voronoi cells. Each cell surrounds a previously generated hypocentre for which the fit to the data has been determined. As the algorithm proceeds new hypocentres are randomly generated in the neighbourhood of those hypocentres with smaller data misfit. In this way all previous hypocentres guide the search, and the more promising regions of parameter space are preferentially sampled.¶The NA procedure makes use of just two tuning parameters. It is possible to choose their values so that the behaviour of the algorithm is similar to that of a contracting irregular grid in 4-D. This is the feature of the algorithm that we exploit for hypocentre location. In experiments with different events and data sources, the NA approach is able to achieve comparable or better levels of data fit than a range of alternative methods; linearised least-squares, genetic algorithms, simulated annealing and a contracting grid scheme. Moreover, convergence was achieved with a substantially reduced number of travel-time/slowness calculations compared with other nonlinear inversion techniques. Even when initial parameter bounds are very loose, the NA procedure produced robust convergence with acceptable levels of data fit. 相似文献