Download or read book Using Multi objective Optimization to Enhance Calibration of Performance Models in the Mechanistic Empirical Pavement Design Guide written by Nima Kargah-Ostadi and published by . This book was released on 2018 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research study devised two scenarios for application of multi-objective optimization to enhance calibration of performance models in the American Association of State Highway and Transportation Officials (AASHTO) AASHTOWare® Pavement ME Design software.(1) In the primary scenario, mean and standard deviation of prediction error are simultaneously minimized to increase accuracy and precision at the same time. In the second scenario, model prediction error on data from Federal Highway Administration’s Long-Term Pavement Performance test sections and error on available accelerated pavement testing data are treated as independent objective functions to be minimized simultaneously. The multi-objective optimization results in a final pool of tradeoff solutions, where none of the viable sets of calibration factors are eliminated prematurely. Exploring the final front results in more reasonable calibration coefficients that could not be identified using single-objective approaches. This report demonstrates the application of engineering judgment and qualitative criteria to select reasonable calibration coefficients from the final pool of solutions that result from the multi-objective optimization. More reasonable calibration factors result in a more justifiable pavement design considering multiple aspects of pavement performance. This investigation revealed that simply evaluating the bias and standard error is not adequate for a comprehensive evaluation of performance prediction models.
Download or read book Mechanistic empirical Pavement Design Guide written by American Association of State Highway and Transportation Officials and published by AASHTO. This book was released on 2008 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Efficient Multi objective Surrogate Optimization of Computationally Expensive Models with Application to Watershed Model Calibration written by Taimoor Akhtar and published by . This book was released on 2015 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis introduces efficient algorithms for multi-objective optimization of computationally expensive simulation optimization problems. Implementation of efficient algorithms and their advantage of use for calibration of complex and deterministic watershed simulation models is also analyzed. GOMORS, a novel parallel multi-objective optimization algorithm involving surrogate modeling via Radial Basis Function approximation, is introduced in Chapter 2. GOMORS is an iterative search algorithm where a multiobjective search utilizing evolution, local search, multi method search and non-dominated sorting is done on the surrogate function to select numerous points for simultaneous expensive evaluations in each algorithm iteration. A novel procedure, "multi-rule selection", is introduced that simultaneously selects evaluation points (which can be computed in parallel) within an algorithm iteration through different metrics. Results are compared against ParEGO and the widely used NSGA-II on numerous test problems including a hypothetical groundwater PDE problem. The results indicate that GOMORS outperforms ParEGO and NSGA-II within a budget of 400 function evaluations. The superiority of performance of GOMORS is more evident for problems involving a large number of decision variables (15-25 decision variables). The second contribution (Chapter 3) to the thesis is a comparative analysis of algorithms for multi-objective calibration of complex watershed models. Since complex watershed models can be computationally expensive, we analyze and compare performance of various algorithms within a limited evaluation budget of 1000 evaluations. The primary aim of the analysis is to assess effectiveness of algorithms in identifying "meaningful trade-offs" for multi-objective watershed model calibration problems within a limited evaluation budget. A new metric, referred as the Distributed Cardinality index, is introduced for quantifying the relative effectiveness of different algorithms in identifying "meaningful tradeoffs". Our results indicate that GOMORS (the algorithm introduced in Chapter 2), outperforms various other algorithms, including ParEGO and AMALGAM, in computing good and meaningful trade-off solutions, within a limited simulation evaluation budget. The third and final contribution (see Chapter 4) to the thesis is MOPLS, a Multi-Objective Parallel Local Stochastic Search algorithm for efficient optimization of computationally expensive problems. MOPLS is an iterative algorithm which incorporates simultaneous local candidate search on response surface models within a synchronous parallel framework to select numerous evaluation points in each iteration. MOPLS was applied to various test problems and multi-objective watershed calibration problems with 4, 8 and 16 synchronous parallel processes and results were compared against GOMORS, ParEGO and AMALGAM. The results indicate that within a limited evaluation budget, MOPLS outperforms ParEGO and AMALGAM for computationally expensive watershed calibration problems, when comparison is made in function evaluations. When parallel speedup is taken into consideration and comparison is made in wall clock time, the results indicate that overall performance of MOPLS is better than GOMORS, ParEGO and AMALGAM.
Download or read book Multiobjective Optimization written by Jürgen Branke and published by Springer. This book was released on 2008-10-18 with total page 481 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multiobjective optimization deals with solving problems having not only one, but multiple, often conflicting, criteria. Such problems can arise in practically every field of science, engineering and business, and the need for efficient and reliable solution methods is increasing. The task is challenging due to the fact that, instead of a single optimal solution, multiobjective optimization results in a number of solutions with different trade-offs among criteria, also known as Pareto optimal or efficient solutions. Hence, a decision maker is needed to provide additional preference information and to identify the most satisfactory solution. Depending on the paradigm used, such information may be introduced before, during, or after the optimization process. Clearly, research and application in multiobjective optimization involve expertise in optimization as well as in decision support. This state-of-the-art survey originates from the International Seminar on Practical Approaches to Multiobjective Optimization, held in Dagstuhl Castle, Germany, in December 2006, which brought together leading experts from various contemporary multiobjective optimization fields, including evolutionary multiobjective optimization (EMO), multiple criteria decision making (MCDM) and multiple criteria decision aiding (MCDA). This book gives a unique and detailed account of the current status of research and applications in the field of multiobjective optimization. It contains 16 chapters grouped in the following 5 thematic sections: Basics on Multiobjective Optimization; Recent Interactive and Preference-Based Approaches; Visualization of Solutions; Modelling, Implementation and Applications; and Quality Assessment, Learning, and Future Challenges.
Download or read book Evolutionary Large Scale Multi Objective Optimization and Applications written by Xingyi Zhang and published by John Wiley & Sons. This book was released on 2024-09-11 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tackle the most challenging problems in science and engineering with these cutting-edge algorithms Multi-objective optimization problems (MOPs) are those in which more than one objective needs to be optimized simultaneously. As a ubiquitous component of research and engineering projects, these problems are notoriously challenging. In recent years, evolutionary algorithms (EAs) have shown significant promise in their ability to solve MOPs, but challenges remain at the level of large-scale multi-objective optimization problems (LSMOPs), where the number of variables increases and the optimized solution is correspondingly harder to reach. Evolutionary Large-Scale Multi-Objective Optimization and Applications constitutes a systematic overview of EAs and their capacity to tackle LSMOPs. It offers an introduction to both the problem class and the algorithms before delving into some of the cutting-edge algorithms which have been specifically adapted to solving LSMOPs. Deeply engaged with specific applications and alert to the latest developments in the field, it’s a must-read for students and researchers facing these famously complex but crucial optimization problems. The book’s readers will also find: Analysis of multi-optimization problems in fields such as machine learning, network science, vehicle routing, and more Discussion of benchmark problems and performance indicators for LSMOPs Presentation of a new taxonomy of algorithms in the field Evolutionary Large-Scale Multi-Objective Optimization and Applications is ideal for advanced students, researchers, and scientists and engineers facing complex optimization problems.
Download or read book Toward Enhancement of Evolutionary Multi and Many objective Optimization written by Amin Ibrahim and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the last three decades, the focus of multi-criteria optimization has been solving problems containing two or three objectives. However, real-world problems generally involve multiple stakeholders and functionalities requiring relatively large number of objectives and decision variables to model these sophisticated problems. In the optimization field, multi-objective problems with four or more objectives are called many-objective problems. Although there are a number of highly successful multi-objective algorithms capable of solving complex two- or three-objective problems, the majority of these algorithms experience significant performance deterioration due-to an increase in the number of solutions required for approximating the entire Pareto-front and the loss of selection pressure required to move non-dominated candidate solutions towards the optimal Pareto-front. Moreover, as the number of objectives increases, visualization of the solution set becomes progressively challenging as well as the applicability of quantitative performance metrics capable of measuring the convergence and diversity of solution become computationally too expensive or unreliable. This thesis explores the challenges associated with solving many-objective optimization problems and proposes novel algorithms, performance measures, and visualization techniques to mitigate these challenges. Firstly, three multi- and many-objective visualization techniques are proposed. These visualization techniques are capable of showing the convergence and distribution of solutions on the Pareto-optimal front, the distribution of solutions along each objective, and relationship among decision variables and objective function values. Secondly, two novel performance measures capable of assessing the distribution and spread of solutions along each objective are proposed. Thirdly, a new reference-based hybrid optimization framework is proposed to allow multiple optimization algorithms to work together to take advantage of their combined benefits. This framework is also capable of extracting a subset of well-distributed solutions from thousands of non-dominated solutions collected during the optimization process of several algorithms. Lastly, the proposed optimization algorithms, visualization techniques, and performance measures are applied to multi-objective renewable energy systems to assess their efficacy when dealing with real-world problems. Experimental results on widely used benchmark and real-world optimization problems indicate that the proposed optimization algorithms, visualization techniques, and performance measures can significantly enhance the solving and decision-making processes involved in multi- or many-objective optimization.
Download or read book Multi Objective Optimization in Theory and Practice I Classical Methods written by Andre A. Keller and published by Bentham Science Publishers. This book was released on 2017-12-13 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multi-Objective Optimization in Theory and Practice is a traditional two-part approach to solving multi-objective optimization (MOO) problems namely the use of classical methods and evolutionary algorithms. This first book is devoted to classical methods including the extended simplex method by Zeleny and preference-based techniques. This part covers three main topics through nine chapters. The first topic focuses on the design of such MOO problems, their complexities including nonlinearities and uncertainties, and optimality theory. The second topic introduces the founding solving methods including the extended simplex method to linear MOO problems and weighting objective methods. The third topic deals with particular structures of MOO problems, such as mixed-integer programming, hierarchical programming, fuzzy logic programming, and bimatrix games. Multi-Objective Optimization in Theory and Practice is a user-friendly book with detailed, illustrated calculations, examples, test functions, and small-size applications in Mathematica® (among other mathematical packages) and from scholarly literature. It is an essential handbook for students and teachers involved in advanced optimization courses in engineering, information science, and mathematics degree programs.
Download or read book Multi Objective Optimization Problems written by Fran Sérgio Lobato and published by Springer. This book was released on 2017-07-03 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is aimed at undergraduate and graduate students in applied mathematics or computer science, as a tool for solving real-world design problems. The present work covers fundamentals in multi-objective optimization and applications in mathematical and engineering system design using a new optimization strategy, namely the Self-Adaptive Multi-objective Optimization Differential Evolution (SA-MODE) algorithm. This strategy is proposed in order to reduce the number of evaluations of the objective function through dynamic update of canonical Differential Evolution parameters (population size, crossover probability and perturbation rate). The methodology is applied to solve mathematical functions considering test cases from the literature and various engineering systems design, such as cantilevered beam design, biochemical reactor, crystallization process, machine tool spindle design, rotary dryer design, among others.
Download or read book High Performance Simulation Based Optimization written by Thomas Bartz-Beielstein and published by . This book was released on 2020 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the state of the art in designing high-performance algorithms that combine simulation and optimization in order to solve complex optimization problems in science and industry, problems that involve time-consuming simulations and expensive multi-objective function evaluations. As traditional optimization approaches are not applicable per se, combinations of computational intelligence, machine learning, and high-performance computing methods are popular solutions. But finding a suitable method is a challenging task, because numerous approaches have been proposed in this highly dynamic field of research. That's where this book comes in: It covers both theory and practice, drawing on the real-world insights gained by the contributing authors, all of whom are leading researchers. Given its scope, if offers a comprehensive reference guide for researchers, practitioners, and advanced-level students interested in using computational intelligence and machine learning to solve expensive optimization problems.
Download or read book Statistical Modeling With Matlab Calibration Models Optimization and Optimization Analysis written by Olsen F. and published by . This book was released on 2016-11-16 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model-Based Calibration Toolbox contains tools for design of experiment, statistical modeling, and calibration of complex systems. The toolbox has two main user interfaces:* Model Browser for design of experiment and statistical modeling* CAGE Browser for analytical calibrationCAGE (CAlibration GEneration) is an easy-to-use graphical interface for calibrating lookup tables for your electronic control unit (ECU). As engines get more complicated, and models of engine behavior more intricate, it is increasingly difficult to rely on intuition alone to calibrate lookup tables. CAGE provides analytical methods for calibrating lookup tables.CAGE uses models of the engine control subsystems to calibrate lookup tables. With CAGE you fill and optimize lookup tables in existing ECU software using models from the Model Browser part of the Model-Based Calibration Toolbox product. From these models, CAGE builds steady-state ECU calibrations. CAGE also compares lookup tables directly to experimental data for validation. CAGE can optimize calibrations with reference to models, including single- and multi-objective optimizations, sum optimizations, user-defined optimizations, and automated tradeoff.
Download or read book Multi objective Optimization in Graphical Models written by Emma Rollón and published by . This book was released on 2008 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Models Calibration Wih Matlab written by Perez C. and published by Createspace Independent Publishing Platform. This book was released on 2017-08-07 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: The MATLAB software include eficient tools for optimization and calibration models. The Model-Based Calibration Toolbox product contains tools for design of experiment, statistical modeling, and calibration of complex systems. The toolbox has two main apps: Model Browser for design of experiment and statistical modeling and CAGE Browser for analytical calibration. CAGE (CAlibration GEneration) is an easy-to-use graphical interface for calibrating lookup tables for your electronic control unit (ECU). As engines get more complicated, and models of engine behavior more intricate, it is increasingly difficult to rely on intuition alone to calibrate lookup tables. CAGE provides analytical methods for calibrating lookup tables. CAGE uses models of the engine control subsystems to calibrate lookup tables. With CAGE you fill and optimize lookup tables in existing ECU software using models from the Model Browser part of the Model-Based Calibration Toolbox product. From these models, CAGE builds steady-state ECU calibrations. CAGE also compares lookup tables directly to experimental data for validation. CAGE can optimize calibrations with reference to models, including single- and multi-objective optimizations, sum optimizations, user-defined optimizations, and automated tradeoff. You can compare your calibrations to experimental data for validation. For example, after completing a calibration, you can import experimental data from a spreadsheet. You can use CAGE to compare your calibration to the data.. This book develops the following topics: - "Model-Based Calibration Toolbox" - "What Is CAGE?" - "Set Up Calibrations" - "Import Models and Calibration Items" - "Setting Up Models" - "Setting Up Tables" - "Creating Tables from a Model" - "Calibration Manager" - "Importing and Exporting Calibrations" - "Feature Calibrations" - "Import a Strategy from Simulink" - "Tradeoff Calibration" - "Point-by-Point Model Tradeoffs"
Download or read book Multi Objective Optimization written by Patrick Siarry and published by . This book was released on 2004 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A New Approach to Multi objective Optimization written by Edward Perez-Reyes and published by . This book was released on 1990 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Multi objective optimization A Complete Guide written by Gerardus Blokdyk and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Modeling and Design of Flexible Pavements and Materials written by Dallas N. Little and published by Springer. This book was released on 2017-09-25 with total page 702 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook lays out the state of the art for modeling of asphalt concrete as the major structural component of flexible pavements. The text adopts a pedagogy in which a scientific approach, based on materials science and continuum mechanics, predicts the performance of any configuration of flexible roadways subjected to cyclic loadings. The authors incorporate state-of the-art computational mechanics to predict the evolution of material properties, stresses and strains, and roadway deterioration. Designed specifically for both students and practitioners, the book presents fundamentally complex concepts in a clear and concise way that aids the roadway design community to assimilate the tools for designing sustainable roadways using both traditional and innovative technologies.
Download or read book Multi objective Optimization for Efficient Environmental Friendly Design of Benchmark and New Processes written by Amit Goyal and published by . This book was released on 2002 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
