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Computationally Efficient Optimization Of Groundwater Remediation Using Derivative Based And Heuristic Algorithms
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Book Synopsis Computationally Efficient Optimization of Groundwater Remediation Using Derivative-based and Heuristic Algorithms by : Raju Marcus Rohde
Download or read book Computationally Efficient Optimization of Groundwater Remediation Using Derivative-based and Heuristic Algorithms written by Raju Marcus Rohde and published by . This book was released on 2002 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Efficient Optimization Of Computationally Expensive Problems Using A New Parallel Algorithm And Response Surface Based Methods by : Amandeep Singh
Download or read book Efficient Optimization Of Computationally Expensive Problems Using A New Parallel Algorithm And Response Surface Based Methods written by Amandeep Singh and published by . This book was released on 2011 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis concerns the development and implementation of efficient optimization algorithms for simulation based functions (real world problems) that are computationally expensive to evaluate. The first contribution is a new parallel algorithm, RODDS for global optimization. RODDS algorithm is a stochastic heuristic global search algorithm, which effectively uses multi-core computers to reduce the computational expense of an optimization problem. The RODDS algorithm introduces the use of hyperspheres in candidate point generation. The optimization search is based on the concept of dynamically changing the dimensions perturbed to direct the search from a global to a local focus. Hyperspheres are used to prevent clustering of candidate points in optimization process to efficiently search the domain. We present numerical results on test problems as well as real world application problems from environmental engineering (groundwater management and watershed calibration) to document RODDS effectiveness when the computational budget is limited. RODDS algorithm achieves efficiencies greater than 1 for most applications which is very significant since implementation of parallel processing usually results in efficiency well below 1. We also present numerical results to show the efficiency of the use of hyperspheres in candidate point generation in RODDS by comparing with a parallel implementation without the hyperspheres. The next contribution is application of Radial basis function (RBF) based methods on computationally expensive optimization problems. We compare the performance of RBF methods with several popular global optimization algorithms (derivative based and heuristic) on two Groundwater superfund remediation sites (Pump and Treat system). These are two field sites Umatilla Chemical Depot (19,728 acres) and Blaine Ammunition Depot (48,800 acres). We present numerical results to indicate that RBF based methods are much more effective algorithms for computationally expensive groundwater problems, followed by a heuristic algorithm DDS. Under limited budget RBF based methods on average outperform traditional methods by an order of 100. The third contribution is a new methodology of integrating a new integer value optimizer (Search over Integers with Tabu (SIT)) with continuous value optimizer (RBF based method) to solve fixed cost problems (which are Mixed Integer value problems, MIVP). Mixed integer value problems (MIVP) in general have large search domain thus the optimization process is computationally very expensive. This approach tries to take advantage of the fact that SIT is effective for optimizing discrete variables, while response surface method is much more efficient for optimizing continuous value variables. This study tries to limit the computational expense of such kind of problems by implementing a Sequential Response Surface method in conjunction with SIT. We present numerical results to show the effectiveness of integration methodology in comparison to Genetic Algorithm based NSGA-II (Deb et. al., 2003) and the MIVP optimizer, NOMAD (Abramson et. al. 2008). The SIT-RBF methodology is shown to be distinctly better than GA (SIT-RBF resulting in 150 times better solution than GA) under limited computational budget.
Book Synopsis Development of Efficient Optimization Algorithms for Groundwater Remediation by : Christine A. Shoemaker
Download or read book Development of Efficient Optimization Algorithms for Groundwater Remediation written by Christine A. Shoemaker and published by . This book was released on 1997 with total page 73 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Dissertation Abstracts International by :
Download or read book Dissertation Abstracts International written by and published by . This book was released on 2002 with total page 854 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Application of Heuristic Optimization to Groundwater Management by : Loren Shawn Matott
Download or read book Application of Heuristic Optimization to Groundwater Management written by Loren Shawn Matott and published by . This book was released on 2007 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: Subsurface flow and contaminant transport models are often used in solving groundwater management problems. Automated optimization involving such models is becoming commonplace, and researchers are increasingly encountering problems for which standard gradient-based search algorithms are inadequate. Such cases have motivated an interest in the use of more robust, but computationally expensive, heuristic algorithms. The research reported in this dissertation advances the state-of-practice of heuristic optimization in groundwater management by applying a variety of heuristic methods to three groundwater management problems: (1) optimization of pump-and-treat containment systems, (2) optimization of multi-layered sorptive barrier systems, and (3) calibration of reactive transport models involving nitrate contamination. These studies were facilitated by the development of a new open source software package for model-independent multi-algorithm optimization, which includes special tools for calibration and model ranking and selection. Overall, the optimization studies make several important research contributions by (1) suggesting methods and guidelines for the effective selection and use of heuristic algorithms, (2) investigating techniques for reducing the computational demand associated with heuristic algorithms, (3) providing general insight into the behavior of the selected problems, (4) utilizing modeling techniques, remediation constraints, and/or parameter representations not previously applied to the selected problems, and (5) introducing a novel optimization software package to the research community.
Book Synopsis American Doctoral Dissertations by :
Download or read book American Doctoral Dissertations written by and published by . This book was released on 2001 with total page 776 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Comparison of Stochastic Radial Basis Function and PEST for Automatic Calibration of Computationally Expensive Groundwater Models with Application to Miyun-Huai-Shun Aquifer by : Ying Wan
Download or read book Comparison of Stochastic Radial Basis Function and PEST for Automatic Calibration of Computationally Expensive Groundwater Models with Application to Miyun-Huai-Shun Aquifer written by Ying Wan and published by . This book was released on 2013 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt: Groundwater numerical models have been widely used as effective tools to analyze and manage water resources. However, the accuracy and reliability of a groundwater numerical model largely depends on model parameters calibration, which is extremely computationally expensive. Therefore, it is highly desirable that efficient optimization algorithms be applied to automatic calibration problems. In this study, we compare the performance of three optimization algorithms and propose a new hybrid method. The algorithms are applied to calibration of a model for part of Beijing water supply. We first outline the three algorithms and briefly describe our hybrid method. The first algorithm referred as PEST in this paper is the Gauss-Marquardt-Levenberg (GML) method including truncated singular value decomposition, which is widely applied in the field of model parameter calibration. As the second one, CMAES_P is a "PEST compatible" implementation of CMA-ES (Covariance Matrix Adaptation Evolution Strategy) global optimization algorithm. PEST derivative-based algorithm and CMAES_P are both encapsulated in the automated parameter optimization software PEST, which has advanced predictive analysis and regularization features to minimize user-specified objective functions. The third one, called Stochastic Radial Basis Function (Stochastic RBF) method, is developed by Regis and Shoemaker (2007), which utilizes radial basis function as the response surface model to approximate the expensive objective function. Our new hybrid method combines Stochastic RBF and PEST derivative-based algorithm, which provides PEST derivative-based algorithm with the starting points found by Stochastic RBF. This paper compares the performances of the aforementioned four algorithms for automatic parameter calibration of a groundwater model on three 28-parameter cases and two synthetic test function calibration problems. We employ the following characteristics as our comparison criteria on all the cases: (1) efficiency in giving good objective function for a given number of function evaluations; (2) performance for different statistical criteria; (3) variability of solutions in multiple trials; (4) improvements if more function evaluations are performed. On the basis of 20 trials, the results indicate that Stochastic RBF is best among the three and CMAES_P is superior to PEST. In addition, our hybrid method still failed to beat Stochastic RBF in highly computationally expensive nonlinear cases. To sum up, our results show that Stochastic RBF method is a more efficient alternative to PEST for automatic parameter calibration of computationally expensive groundwater models. ii.
Book Synopsis Reducing the Vulnerability of Societies to Water Related Risks at the Basin Scale by : Andreas Schumann
Download or read book Reducing the Vulnerability of Societies to Water Related Risks at the Basin Scale written by Andreas Schumann and published by . This book was released on 2007 with total page 468 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume comprises the keynotes and the peer reviewed papers that were selected from the four core topics of the 3rd international IWRM symposium Reducing the vulnerability of societies to water related risks at the basin scale, that was held at the Ruhr-University Bochum, Germany, from 26-28 September 2006: (i) From headwaters to the mouth - vulnerable interactions between landscapes, water and societies (ii) Flood risk - flood vulnerability - flood protection (iii) (iv) Water management as a solution
Book Synopsis Final Degree List by : Cornell University. Graduate School
Download or read book Final Degree List written by Cornell University. Graduate School and published by . This book was released on 2002 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Developing Parsimonious and Efficient Algorithms for Water Resources Optimization Problems by : Masoud Asadzadeh Esfahani
Download or read book Developing Parsimonious and Efficient Algorithms for Water Resources Optimization Problems written by Masoud Asadzadeh Esfahani and published by . This book was released on 2012 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the current water resources scientific literature, a wide variety of engineering design problems are solved in a simulation-optimization framework. These problems can have single or multiple objective functions and their decision variables can have discrete or continuous values. The majority of current literature in the field of water resources systems optimization report using heuristic global optimization algorithms, including evolutionary algorithms, with great success. These algorithms have multiple parameters that control their behavior both in terms of computational efficiency and the ability to find near globally optimal solutions. Values of these parameters are generally obtained by trial and error and are case study dependent. On the other hand, water resources simulation-optimization problems often have computationally intensive simulation models that can require seconds to hours for a single simulation. Furthermore, analysts may have limited computational budget to solve these problems, as such, the analyst may not be able to spend some of the computational budget to fine-tune the algorithm settings and parameter values. So, in general, algorithm parsimony in the number of parameters is an important factor in the applicability and performance of optimization algorithms for solving computationally intensive problems. A major contribution of this thesis is the development of a highly efficient, single objective, parsimonious optimization algorithm for solving problems with discrete decision variables. The algorithm is called Hybrid Discrete Dynamically Dimensioned Search, HD-DDS, and is designed based on Dynamically Dimensioned Search (DDS) that was developed by Tolson and Shoemaker (2007) for solving single objective hydrologic model calibration problems with continuous decision variables. The motivation for developing HD-DDS comes from the parsimony and high performance of original version of DDS. Similar to DDS, HD-DDS has a single parameter with a robust default value. HD-DDS is successfully applied to several benchmark water distribution system design problems where decision variables are pipe sizes among the available pipe size options. Results show that HD-DDS exhibits superior performance in specific comparisons to state-of-the-art optimization algorithms. The parsimony and efficiency of the original and discrete versions of DDS and their successful application to single objective water resources optimization problems with discrete and continuous decision variables motivated the development of a multi-objective optimization algorithm based on DDS. This algorithm is called Pareto Archived Dynamically Dimensioned Search (PA-DDS). The algorithm parsimony is a major factor in the design of PA-DDS. PA-DDS has a single parameter from its search engine DDS. In each iteration, PA-DDS selects one archived non-dominated solution and perturbs it to search for new solutions. The solution perturbation scheme of PA-DDS is similar to the original and discrete versions of DDS depending on whether the decision variable is discrete or continuous. So, PA-DDS can handle both types of decision variables. PA-DDS is applied to several benchmark mathematical problems, water distribution system design problems, and water resources model calibration problems with great success. It is shown that hypervolume contribution, HVC1, as defined in Knowles et al. (2003) is the superior selection metric for PA-DDS when solving multi-objective optimization problems with Pareto fronts that have a general (unknown) shape. However, one of the main contributions of this thesis is the development of a selection metric specifically designed for solving multi-objective optimization problems with a known or expected convex Pareto front such as water resources model calibration problems. The selection metric is called convex hull contribution (CHC) and makes the optimization algorithm sample solely from a subset of archived solutions that form the convex approximation of the Pareto front. Although CHC is generally applicable to any stochastic search optimization algorithm, it is applied to PA-DDS for solving six water resources calibration case studies with two or three objective functions.
Book Synopsis Applications of Multi-objective, Mixed-integer and Hybrid Global Optimization Algorithms for Computationally Expensive Groundwater Problems by : Ying Wan
Download or read book Applications of Multi-objective, Mixed-integer and Hybrid Global Optimization Algorithms for Computationally Expensive Groundwater Problems written by Ying Wan and published by . This book was released on 2015 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research focuses on the development and implementation of e cient optimization algorithms that can solve a range of computationally expensive groundwater simulationoptimization problems. Because groundwater model evaluations are expensive, it is important to find accurate solutions with relatively few function evaluations. As a result, all the algorithms tested in this research are evaluated on a limited computation budget. The first contribution to the thesis is a comparative evaluation of a novel multi-objective optimization algorithm, GOMORS, to three other popular multi-objective optimization methods on applications to groundwater management problems within a limited number of objective function evaluations. GOMORS involves surrogate modeling via Radial Basis Function approximation and evolutionary strategies. The primary aim of the analysis is to assess the effectiveness of multi-objective algorithms in groundwater remediation management through multi-objective optimization within a limited evaluation budget. Three sets of dual objectives are evaluated. The objectives include minimization of cost, pollution mass remaining/pollution concentration, and cleanup time. Our results indicate that the overall performance of GOMORS is better than three other algorithms, AMALGAM, BORG and NSGA-II, in identifying good trade-off solutions. Furthermore, GOMORS incorporates modest parallelization to make it even more e cient. The next contribution is application of SO-MI, a surrogate model-based algorithm designed for computationally expensive nonlinear and multimodal mixed-integer black-box optimization problems, to solve groundwater remediation design problems (NL-MIP). SO-MI utilizes surrogate models to guide the search thus save the expensive function evaluation budget, and is able to find accurate solutions with relatively few function evaluations. We present numerical results to show the effectiveness and e ciency of SO-MI in comparison to Genetic Algorithm and NOMAD, which are two popular mixed-integer optimization algorithms. The results indicate that SO-MI is statistically better than GA and NOMAD in both study cases. Chapter 4 describes DYCORS-PEST, a novel method developed for high dimensional, computationally expensive, multimodal calibration problems when the computation budget is limited. This method integrates a local optimizer PEST into a global optimization framework DYCORS. The novelty of DYCORS-PEST is that it uses a memetic approach to improve the accuracy of the solution in which DYCORS selects the point at which the search switches to use of the local method PEST and when it switches back to the global phase. Since PEST is a very e cient and widely used local search algorithm for groundwater model calibration, incorporating PEST into DYCORS-PEST is a good enhancement for PEST and easy for PEST users to learn. DYCORS-PEST achieves the goal of solving the computationally expensive black-box problem by forming a response surface of the expensive function, thus reducing the number of required expensive function evaluations for finding accurate solutions. The key feature of the global search method in DYCORS-PEST is that the number of decision variables being perturbed is dynamically adjusted in each iteration in order to be more effective for higher dimensional problems. Application of DYCORS-PEST to two 28parameter groundwater calibration problems indicate this new method outperforms PEST by a large margin for high dimensional, computationally expensive, groundwater calibration problems.
Book Synopsis Optimizing Groundwater Remediation Designs Using Dynamic Meta-Models and Genetic Algorithms by : Shengquan Yan
Download or read book Optimizing Groundwater Remediation Designs Using Dynamic Meta-Models and Genetic Algorithms written by Shengquan Yan and published by . This book was released on 2006 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Groundwater Remediation Optimization Using Artificial Neural Networks by :
Download or read book Groundwater Remediation Optimization Using Artificial Neural Networks written by and published by . This book was released on 1998 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt: One continuing point of research in optimizing groundwater quality management is reduction of computational burden which is particularly limiting in field-scale applications. Often evaluation of a single pumping strategy, i.e. one call to the groundwater flow and transport model (GFTM) may take several hours on a reasonably fast workstation. For computational flexibility and efficiency, optimal groundwater remediation design at Lawrence Livermore National Laboratory (LLNL) has relied on artificial neural networks (ANNS) trained to approximate the outcome of 2-D field-scale, finite difference/finite element GFTMs. The search itself has been directed primarily by the genetic algorithm (GA) or the simulated annealing (SA) algorithm. This approach has advantages of (1) up to a million fold increase in speed of remediation pattern assessment during the searches and sensitivity analyses for the 2-D LLNL work, (2) freedom from sequential runs of the GFTM (enables workstation farming), and (3) recycling of the knowledge base (i.e. runs of the GFTM necessary to train the ANNS). Reviewed here are the background and motivation for such work, recent applications, and continuing issues of research.
Book Synopsis Optimization in Science and Engineering by : Themistocles M. Rassias
Download or read book Optimization in Science and Engineering written by Themistocles M. Rassias and published by Springer. This book was released on 2014-05-29 with total page 611 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimization in Science and Engineering is dedicated in honor of the 60th birthday of Distinguished Professor Panos M. Pardalos. Pardalos’s past and ongoing work has made a significant impact on several theoretical and applied areas in modern optimization. As tribute to the diversity of Dr. Pardalos’s work in Optimization, this book comprises a collection of contributions from experts in various fields of this rich and diverse area of science. Topics highlight recent developments and include: Deterministic global optimization Variational inequalities and equilibrium problems Approximation and complexity in numerical optimization Non-smooth optimization Statistical models and data mining Applications of optimization in medicine, energy systems, and complex network analysis This volume will be of great interest to graduate students, researchers, and practitioners, in the fields of optimization and engineering.
Download or read book Hydroinformatics written by Praveen Kumar and published by CRC Press. This book was released on 2005-11-02 with total page 619 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern hydrology is more interdisciplinary than ever. Staggering amounts and varieties of information pour in from GIS and remote sensing systems every day, and this information must be collected, interpreted, and shared efficiently. Hydroinformatics: Data Integrative Approaches in Computation, Analysis, and Modeling introduces the tools, approache
Book Synopsis Comparing Methods for Bioremediation Policy Cost Optimization by : José Antonio Aponte
Download or read book Comparing Methods for Bioremediation Policy Cost Optimization written by José Antonio Aponte and published by . This book was released on 2006 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Computational Optimization, Methods and Algorithms by : Slawomir Koziel
Download or read book Computational Optimization, Methods and Algorithms written by Slawomir Koziel and published by Springer. This book was released on 2011-06-17 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational optimization is an important paradigm with a wide range of applications. In virtually all branches of engineering and industry, we almost always try to optimize something - whether to minimize the cost and energy consumption, or to maximize profits, outputs, performance and efficiency. In many cases, this search for optimality is challenging, either because of the high computational cost of evaluating objectives and constraints, or because of the nonlinearity, multimodality, discontinuity and uncertainty of the problem functions in the real-world systems. Another complication is that most problems are often NP-hard, that is, the solution time for finding the optimum increases exponentially with the problem size. The development of efficient algorithms and specialized techniques that address these difficulties is of primary importance for contemporary engineering, science and industry. This book consists of 12 self-contained chapters, contributed from worldwide experts who are working in these exciting areas. The book strives to review and discuss the latest developments concerning optimization and modelling with a focus on methods and algorithms for computational optimization. It also covers well-chosen, real-world applications in science, engineering and industry. Main topics include derivative-free optimization, multi-objective evolutionary algorithms, surrogate-based methods, maximum simulated likelihood estimation, support vector machines, and metaheuristic algorithms. Application case studies include aerodynamic shape optimization, microwave engineering, black-box optimization, classification, economics, inventory optimization and structural optimization. This graduate level book can serve as an excellent reference for lecturers, researchers and students in computational science, engineering and industry.
