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Title: Optimal power flow and network loadability using feedback-based self-adaptive differential evolution and multiobjective algorithms
Creator: Alharbi, Fares Theyab A.
Date: 2018
Collection: Electronic Theses & Dissertations
Description: In modern electrical grids, planning and operation processes require efficient optimization tools. Optimal placement and sizing of Flexible AC transmission system (FACTS) devices, renewable energy resources, and energy storage units, to name a few, are optimization tasks in the planning process. Minimizing the cost of generated power from committed generators in the operation process is an important part of power system operations. For power system optimization problems, several optimization...
Show moreIn modern electrical grids, planning and operation processes require efficient optimization tools. Optimal placement and sizing of Flexible AC transmission system (FACTS) devices, renewable energy resources, and energy storage units, to name a few, are optimization tasks in the planning process. Minimizing the cost of generated power from committed generators in the operation process is an important part of power system operations. For power system optimization problems, several optimization algorithms have been proposed and used in the past two decades. However, the need for efficient optimization algorithms customized to power system problems still exists. The research reported in this thesis develops novel evolutionary optimization approaches for two applications: optimal power flow (OPF) and optimal placement and sizing of FACTS to enhance electrical network loadability. For optimal power flow, two new feedback-based self-adaptive differential evolution algorithms are proposed. Prior to applying the proposed methods to the power system test cases, they are tested on standard mathematical benchmark problems. The self-adaptive differential evolution algorithms showed significant improvement in the benchmark problems compared to other algorithms. More importantly, in this work, the feedback-based self-adaptive differential evolution algorithms demonstrated good improvement in results and in convergence rate in several power system test cases. To enhance the loadability of an electrical network, a new multiobjective-based frame work is proposed for optimal placement and sizing of FACTS devices. The proposed method has been applied to commonly used FACTS devices, thyristor-controlled series controllers (TCSCs), and demonstrated excellent results in the electrical loading margins as well as the investment costs compared to other available methods.
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Title: Simulation-multicriteria optimization technique as a decision support system for rice production
Creator: Alocilja, Evangelyn C.
Date: 1987
Collection: Electronic Theses & Dissertations

Title: Optimal singular control theory with application to vehicular braking
Creator: Scherba, Michael Bishop
Date: 1969
Collection: Electronic Theses & Dissertations

Title: Achieving consistent evolution across isometrically equivalent search spaces
Creator: Patton, Arnold L.
Date: 2004
Collection: Electronic Theses & Dissertations

Title: Multipoint optimization of a 3D radial compressor impeller
Creator: Kowalksi, Steven C.
Date: 2005
Collection: Electronic Theses & Dissertations

Title: Optimization of accelerator parameters using normal form methods on high-order transfer maps
Creator: Snopok, Pavel
Date: 2007
Collection: Electronic Theses & Dissertations

Title: Artificial neural networks for constrained and unconstrained optimization
Creator: Chen, Jiahan
Date: 1992
Collection: Electronic Theses & Dissertations

Title: Hierarchical topology optimization problems in three-dimensions
Creator: DeRose, Giuseppe C. A. (Giuseppe Carmine Americo)
Date: 1996
Collection: Electronic Theses & Dissertations

Title: Expected utility maximization and the theory of multi product banking firm under uncertainty
Creator: Elyasiani, Elyas
Date: 1979
Collection: Electronic Theses & Dissertations

Title: Computer aided optimization of nonlinear servomechanisms employing a directed search of multiparameter component libraries and statistical tolerancing
Creator: Chubb, Bruce A.
Date: 1969
Collection: Electronic Theses & Dissertations

Title: Exponentiated gradient portfolios in continuous trading
Creator: White, Alexander K.
Date: 1999
Collection: Electronic Theses & Dissertations

Title: An optimization framework to tune a lattice model for improving crashworthiness using surrogates
Creator: Kumar, Rakesh
Date: 2001
Collection: Electronic Theses & Dissertations

Title: Neural networks for nonlinear programming
Creator: Maa, Chia-Yiu
Date: 1991
Collection: Electronic Theses & Dissertations

Title: Sustainable evolutionary algorithms and scalable evolutionary synthesis of dynamic systems
Creator: Hu, Jianjun
Date: 2004
Collection: Electronic Theses & Dissertations

Title: Metamodeling framework for simultaneous multi-objective optimization using efficient evolutionary algorithms
Creator: Roy, Proteek Chandan
Date: 2019
Collection: Electronic Theses & Dissertations
Description: Most real-world problems are comprised of multiple conflicting objectives and solutions to those problems are multiple Pareto-optimal trade-off solutions. The main challenge of these practical problems is that the objectives and constraints do not have any closed functional forms and they are expensive for computation as well. Objectives coming from finite element analysis, computational fluid dynamics software, network flow simulators, crop modeling, weather modeling or any other simulations...
Show moreMost real-world problems are comprised of multiple conflicting objectives and solutions to those problems are multiple Pareto-optimal trade-off solutions. The main challenge of these practical problems is that the objectives and constraints do not have any closed functional forms and they are expensive for computation as well. Objectives coming from finite element analysis, computational fluid dynamics software, network flow simulators, crop modeling, weather modeling or any other simulations which involve partial differential equations are good examples of expensive problems. These problems can also be regarded as l03000300ow-budget'' problems since only a few solution evaluations can be performed given limited time. Nevertheless, parameter estimation and optimization of objectives related to these simulations require a good number of solution evaluations to come up with better parameters or a reasonably good trade-off front. To provide an efficient search process within a limited number of exact evaluations, metamodel-assisted algorithms have been proposed in the literature. These algorithms attempt to construct a computationally inexpensive representative model of the problem, having the same global optima and thereby providing a way to carry out the optimization in metamodel space in an efficient way. Population-based methods like evolutionary algorithms have become standard for solving multi-objective problems and recently Metamodel-based evolutionary algorithms are being used for solving expensive problems. In this thesis, we would like to address a few challenges of metamodel-based optimization algorithms and propose some efficient and innovative ways to construct these algorithms. To approach efficient design of metamodel-based optimization algorithm, one needs to address the choice of metamodeling functions. The most trivial way is to build metamodels for each objective and constraint separately. But we can reduce the number of metamodel constructions by using some aggregated functions and target either single or multiple optima in each step. We propose a taxonomy of possible metamodel-based algorithmic frameworks which not only includes most algorithms from the literature but also suggests some new ones. We improve each of the frameworks by introducing trust region concepts in the multi-objective scenario and present two strategies for building trust regions. Apart from addressing the main bottleneck of the limited number of solution evaluations, we also propose efficient non-dominated sorting methods that further reduce computational time for a basic step of multi-objective optimization. We have carried out extensive experiments over all representative metamodeling frameworks and shown that each of them can solve a good number of test problems. We have not tried to tune the algorithmic parameters yet and it remains as our future work. Our theoretical analyses and extensive experiments suggest that we can achieve efficient metamodel-based multi-objective optimization algorithms for solving test as well as real-world expensive and low-budget problems.
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Title: Optimal control of linear discrete macroeconomic systems
Creator: Paryani, Kioumars, 1941-
Date: 1972
Collection: Electronic Theses & Dissertations

Title: Optimizing message to virtual link assignment in Avionics Full-Duplex Switched Ethernet networks
Creator: Klonowski, Joseph
Date: 2019
Collection: Electronic Theses & Dissertations
Description: "Avionics Full-Duplex Switched Ethernet (AFDX) is an Ethernet-based data network that provides deterministic performance, high reliability, and lower costs and development time by utilizing commercial off-the-shelf networking components. As AFDX networks have become of the network are continually being evaluated. There are two main types of solutions to improving network performance: changes to the physical layer and changes to the logical layer. Because the physical network is setup prior to...
Show more"Avionics Full-Duplex Switched Ethernet (AFDX) is an Ethernet-based data network that provides deterministic performance, high reliability, and lower costs and development time by utilizing commercial off-the-shelf networking components. As AFDX networks have become of the network are continually being evaluated. There are two main types of solutions to improving network performance: changes to the physical layer and changes to the logical layer. Because the physical network is setup prior to defining the data that is transferred on the network, logical layer optimization becomes important and is often the only viable solution. Previous research has explored optimization of different aspects of the logical solution for a given target (whether it be latency or bandwidth), however, an approach for a customizable target using optimization techniques has not been attempted. In this work, we provide an overview of AFDX networks and discuss factors engineers consider while optimizing the network. Previously researched solutions are evaluated for effectiveness. We identify the need for an optimization solution that allows for a customizable objective to account for both message latency and bandwidth. To fill this gap, we consider the problem of assigning messages to virtual links, which are configurable, logical unidirectional links from publishing end systems to one or more subscribing end systems. We propose a flexible framework based on particle swarm optimization (PSO) that performs message to virtual link assignment in AFDX networks to optimize a user-defined objective. We discuss and provide results on PSO optimization for a range of hyperparameters. Finally, results for a sample swarm are presented to prove the feasibility and usefulness of the proposed approach."--Page ii.
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Title: Exploiting smoothness in statistical learning, sequential prediction, and stochastic optimization
Creator: Mahdavi, Mehrdad
Date: 2014
Collection: Electronic Theses & Dissertations
Description: In the last several years, the intimate connection between convex optimization and learning problems, in both statistical and sequential frameworks, has shifted the focus of algorithmic machine learning to examine this interplay. In particular, on one hand, this intertwinement brings forward new challenges in reassessment of the performance of learning algorithms including generalization and regret bounds under the assumptions imposed by convexity such as analytical properties of loss...
Show moreIn the last several years, the intimate connection between convex optimization and learning problems, in both statistical and sequential frameworks, has shifted the focus of algorithmic machine learning to examine this interplay. In particular, on one hand, this intertwinement brings forward new challenges in reassessment of the performance of learning algorithms including generalization and regret bounds under the assumptions imposed by convexity such as analytical properties of loss functions (e.g., Lipschitzness, strong convexity, and smoothness). On the other hand, emergence of datasets of an unprecedented size, demands the development of novel and more efficient optimization algorithms to tackle large-scale learning problems. The overarching goal of this thesis is to reassess the smoothness of loss functions in statistical learning, sequential prediction/online learning, and stochastic optimization and explicate its consequences. In particular we examine how leveraging smoothness of loss function could be beneficial or detrimental in these settings in terms of sample complexity, statistical consistency, regret analysis, and convergence rate. In the statistical learning framework, we investigate the sample complexity of learning problems when the loss function is smooth and strongly convex and the learner is provided with the target risk as a prior knowledge. We establish that under these assumptions, by exploiting the smoothness of loss function, we are able to improve the sample complexity of learning exponentially. Furthermore, the proof of our results is constructive and is rooted in a properly designed stochastic optimization algorithm which could be of significant practical importance. We also investigate the smoothness from the viewpoint of statistical consistency and show that in sharp contrast to optimization and generalization where the smoothness is favorable because of its computational and theoretical virtues, the smoothness of surrogate loss function might deteriorate the binary excess risk. Motivated by this negative result, we provide a unified analysis of three types of errors including optimization error, generalization bound, and the error in translating convex excess risk into a binary excess risk, and underline the conditions that smoothness might be preferred.We then turn to elaborate the importance of smoothness in sequential prediction/online learning. We introduce a new measure to assess the performance of online learning algorithms which is referred to as gradual variation. The gradual variation is measured by the sum of the distances between every two consecutive loss functions and is more suitable for gradually evolving environments such as stock prediction. Under smoothness assumption, we devise novel algorithms for online convex optimization with regret bounded by gradual variation. The proposed algorithms can take advantage of benign sequences and at the same time protect against the adversarial sequences of loss functions. Finally, we investigate how to exploit the smoothness of loss function in convex optimization. Unlike the optimization methods based on full gradients, the smoothness assumption was not exploited by most of the existing stochastic optimization methods. We propose a novel optimization paradigm that is referred to as mixed optimization which interpolates between stochastic and full gradient methods and is able to exploit the smoothness of loss functions to obtain faster convergence rates in stochastic optimization, and condition number independent accesses of full gradients in deterministic optimization. The key underlying insight of mixed optimization is to utilize infrequent full gradients of the objective function to progressively reduce the variance of the stochastic gradients. These results show an intricate interplay between stochastic and deterministic convex optimization to take advantages of their individual merits. We also propose efficient projection-free optimization algorithms to tackle the computational challenge arising from the projection steps which are required at each iteration of most existing gradient based optimization methods to ensure the feasibility of intermediate solutions. In stochastic optimization setting, by introducing and leveraging smoothness, we develop novel methods which only require one projection at the final iteration. In online learning setting, we consider online convex optimization with soft constraints where the constraints are allowed to be satisfied on long term. We show that by compromising on the learner's regret, one can devise efficient online learning algorithms with sub-linear bound on both the regret and the violation of the constraints
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Title: IFLMAPS : an interactive farm level marketing and production simulator intended for research, teaching and extension applications
Creator: Rister, M. Edward (Milton Edward)
Date: 1981
Collection: Electronic Theses & Dissertations

Title: Optimization of sub components within a large system
Creator: Halepatali, Praveen
Date: 2004
Collection: Electronic Theses & Dissertations

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