Dissertations / Theses on the topic 'Global Optimization'
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Singer, Adam B. "Global dynamic optimization." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/28662.
Full textIncludes bibliographical references (p. 247-256).
(cont.) on a set composed of the Cartesian product between the parameter bounds and the state bounds. Furthermore, I show that the solution of the differential equations is affine in the parameters. Because the feasible set is convex pointwise in time, the standard result that a convex function composed with an affine function remains convex yields the desired result that the integrand is convex under composition. Additionally, methods are developed using interval arithmetic to derive the exact state bounds for the solution of a linear dynamic system. Given a nonzero tolerance, the method is rigorously shown to converge to the global solution in a finite time. An implementation is developed, and via a collection of case studies, the technique is shown to be very efficient in computing the global solutions. For problems with embedded nonlinear dynamic systems, the analysis requires a more sophisticated composition technique attributed to McCormick. McCormick's composition technique provides a method for computing a convex underestimator for for the integrand given an arbitrary nonlinear dynamic system provided that convex underestimators and concave overestimators can be given for the states. Because the states are known only implicitly via the solution of the nonlinear differential equations, deriving these convex underestimators and concave overestimators is a highly nontrivial task. Based on standard optimization results, outer approximation, the affine solution to linear dynamic systems, and differential inequalities, I present a novel method for constructing convex underestimators and concave overestimators for arbitrary nonlinear dynamic systems ...
My thesis focuses on global optimization of nonconvex integral objective functions subject to parameter dependent ordinary differential equations. In particular, efficient, deterministic algorithms are developed for solving problems with both linear and nonlinear dynamics embedded. The techniques utilized for each problem classification are unified by an underlying composition principle transferring the nonconvexity of the embedded dynamics into the integral objective function. This composition, in conjunction with control parameterization, effectively transforms the problem into a finite dimensional optimization problem where the objective function is given implicitly via the solution of a dynamic system. A standard branch-and-bound algorithm is employed to converge to the global solution by systematically eliminating portions of the feasible space by solving an upper bounding problem and convex lower bounding problem at each node. The novel contributions of this work lie in the derivation and solution of these convex lower bounding relaxations. Separate algorithms exist for deriving convex relaxations for problems with linear dynamic systems embedded and problems with nonlinear dynamic systems embedded. However, the two techniques are unified by the method for relaxing the integral in the objective function. I show that integrating a pointwise in time convex relaxation of the original integrand yields a convex underestimator for the integral. Separate composition techniques, however, are required to derive relaxations for the integrand depending upon the nature of the embedded dynamics; each case is addressed separately. For problems with embedded linear dynamic systems, the nonconvex integrand is relaxed pointwise in time
by Adam Benjamin Singer.
Ph.D.
Ruan, Ning. "Global optimization for nonconvex optimization problems." Thesis, Curtin University, 2012. http://hdl.handle.net/20.500.11937/1936.
Full textMoore, Roxanne Adele. "Value-based global optimization." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44750.
Full textBirbil, Sevket Ilker. "Stochastic Global Optimization Techniques." NCSU, 2002. http://www.lib.ncsu.edu/theses/available/etd-20020403-171452.
Full textIn this research, a novel population-based global optimization method has been studied. The method is called Electromagnetism-like Mechanism or in short EM. The proposed method mimicks the behavior of electrically charged particles. In other words, a set of points is sampled from the feasible region and these points imitate the role of the charged particles in basic electromagnetism. The underlying idea of the method is directing sample points toward local optimizers, which point out attractive regions of the feasible space.The proposed method has been applied to different test problems from the literature. Moreover, the viability of the method has been tested by comparing its results with other reported results from the literature. Without using the higher order information, EM has converged rapidly (in terms of the number of function evaluations) to the global optimum and produced highly efficient results for problems of varying degree of difficulty.After a systematic study of the underlying stochastic process, the proof of convergence to the global optimum has been given for the proposed method. The thrust of the proof has been to show that in the limit, at least one of the points in the population moves to the neighborhood of the global optimum with probability one.The structure of the proposed method is very flexible permitting the easy development of variations. Capitalizing on this, several variants of the proposed method has been developed and compared with the other methods from the literature. These variants of EM have been able to provide accurate answers to selected problems and in many cases have been able to outperform other well-known methods.
Gattupalli, Rajeswar R. "Advances in global optimization /." View online ; access limited to URI, 2008. http://0-digitalcommons.uri.edu.helin.uri.edu/dissertations/AAI3314454.
Full textZehnder, Nino. "Global optimization of laminated structures /." Zürich : ETH, 2008. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=17573.
Full textQuttineh, Nils-Hassan. "Algorithms for Costly Global Optimization." Licentiate thesis, Mälardalen University, School of Education, Culture and Communication, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-5970.
Full textThere exists many applications with so-called costly problems, which means that the objective function you want to maximize or minimize cannot be described using standard functions and expressions. Instead one considers these objective functions as ``black box'' where the parameter values are sent in and a function value is returned. This implies in particular that no derivative information is available.The reason for describing these problems as expensive is that it may take a long time to calculate a single function value. The black box could, for example, solve a large system of differential equations or carrying out a heavy simulation, which can take anywhere from several minutes to several hours!These very special conditions therefore requires customized algorithms. Common optimization algorithms are based on calculating function values every now and then, which usually can be done instantly. But with an expensive problem, it may take several hours to compute a single function value. Our main objective is therefore to create algorithms that exploit all available information to the limit before a new function value is calculated. Or in other words, we want to find the optimal solution using as few function evaluations as possible.A good example of real life applications comes from the automotive industry, where on the development of new engines utilize advanced models that are governed by a dozen key parameters. The goal is to optimize the model by changing the parameters in such a way that the engine becomes as energy efficient as possible, but still meets all sorts of demands on strength and external constraints.
Schonlau, Matthias. "Computer experiments and global optimization." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq22234.pdf.
Full textAl-Mharmah, Hisham. "Global optimization of stochastic functions." Diss., Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/25665.
Full textOulmane, Mourad. "On-Chip global interconnect optimization." Thesis, McGill University, 2001. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=33985.
Full textAccurate moment matching techniques for computing the RC delays and transition times are used in addition to an accurate CMOS inverter/repeater delay model that takes into account short channel effects that are prevalent in deep submicron (DSM) technologies. In particular, a new delay metric, based on the first two moments of the impulse response of the interconnect RC circuit, is derived. Also, a new empirical ramp approximation that takes into account the inherent asymmetry of signals in signal distribution networks in DSM technologies is presented.
Stephens, Chris. "Global optimization: theory versus practice." Thesis, University of Canterbury. Mathematics, 1997. http://hdl.handle.net/10092/8430.
Full textNewbern, Jeffrey Lawrence. "Global optimization of dither arrays." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/10894.
Full textIncludes bibliographical references (p. 59-60).
by Jeffrey Newbern.
M.Eng.
Theodosopoulos, Theodore. "Stochastic models for global optimization." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/11404.
Full textIncludes bibliographical references (p. 61-63).
by Theodore Vassilios Theodosopoulos.
Ph.D.
Wechsung, Achim. "Global optimization in reduced space." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/87131.
Full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 203-216).
Optimization is a key activity in any engineering discipline. Global optimization methods, in particular, strive to solve nonconvex problems, which often arise in chemical engineering, and deterministic algorithms such as branch-and-bound provide a certificate of optimality for the identified solution. Unfortunately, the worst-case runtime of these algorithms is exponential in the problem dimension. This leads to the notion of reduced-space problem formulations where either the number of variables that the algorithm branches on is reduced or only the actual degrees of freedom are visible to the optimization algorithms, following a partition of the variables into independent and dependent ones. This approach introduces new challenges though: McCormick relaxations, which are very easily applied in this setting, can be nonsmooth, the minima are very likely to be unconstrained causing the cluster problem and the information contained in the constraints is not as readily exploited. In this thesis, several advances to both theory and methods are reported. First, a new analysis of the cluster problem is provided reaffirming the importance of second-order convergent bounding methods. The cluster problem refers to the phenomenon whereby a large number of boxes in the vicinity of a minimum are visited by branch-and-bound algorithms. In particular, it is shown that tighter relaxations can lead to a significant reduction in the number of boxes visited. Next, a constraint propagation technique for intervals is extended to McCormick relaxations. This reverse McCormick update utilizes information in the constraints and improves relaxations of the dependent variables, which can be used to either strengthen the relaxations of the feasible set or, using generalized McCormick relaxations, to construct reduced-space relaxations of the objective function. Third, a second-order convergent interval bounding method for the zeros of parametric nonlinear systems of equations is presented. This is useful to provide second-order convergent interval information to generalized McCormick relaxations, e.g., in the reverse propagation scheme. Fourth, the theory underpinning McCormick relaxations is extended to a class of discontinuous functions. It is further shown that branch-and-bound algorithms still possess their convergence properties.
by Achim Wechsung.
Ph. D.
Lee, Cha Kun. "Global optimization of hybrid systems." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/37276.
Full textIncludes bibliographical references (p. 339-353).
Systems that exhibit both discrete state and continuous state dynamics are called hybrid systems. In most nontrivial cases, these two aspects of system behavior interact to such a significant extent that they cannot be decoupled effectively by any kind of abstraction and must be analyzed simultaneously. Hybrid system models are important in many areas of science and engineering, including flip-flops and latching relays, manufacturing systems, air-traffic management systems, controller synthesis, switched systems, chemical process systems, signaling and decision making mechanisms in (biological) cells, robotic systems, safety interlock systems, and embedded systems. The primary focus of this thesis is to explore deterministic methods for the global optimization of hybrid systems. While the areas of modeling, simulation and sensitivity analysis of hybrid systems have received much attention, there are many challenging difficulties associated with the optimization of such systems. The contents of this thesis represent the first steps toward deterministic global optimization of hybrid systems in the continuous time domain. There are various reasons for wanting to solve optimization problems globally.
(cont.) In particular, there are many applications which demand that the global solution be found, for example, formal safety verification problems and parameter estimation problems. In the former case, a suboptimal local solution could falsely indicate that all safety specifications are met, leading to disastrous consequences if, in actuality, a global solution exists which provides a counter example that violates some safety specification. In the latter case, a suboptimal local solution could falsely indicate that a proposed model structure did not match experimental data in a statistically significant manner, leading to the false rejection of a valid model structure. In addition, for many optimization problems in engineering, the presence of nonlinear equality constraints makes the optimization problem nonconvex such that local optimization methods can often fail to produce a single feasible point, even though the problem is indeed feasible. The control parameterization framework is employed for the solution of the optimization problem with continuous time hybrid systems embedded. A major difficulty of such a framework lies in the fact that the mode sequence of the embedded hybrid system changes in the space of the optimization decision variables for most nontrivial problems.
(cont.) This makes the resulting optimization problem nonsmooth because the parametric sensitivities of the hybrid system do not exist everywhere, thus invalidating efficient gradient based optimization solvers. In this thesis, the general optimization problem is decomposed into three subproblems, and tackled individually: (a) when the mode sequence is fixed, and the transition times are fixed; (b) when the mode sequence is allowed to vary, and the transition times are fixed; and (c) when the mode sequence is fixed, and the transition times are allowed to vary. Because even these subproblems are nontrivial to solve, this thesis focuses on hybrid systems with linear time varying ordinary differential equations describing the continuous dynamics, and proposes various methods to exploit the linear structure. However, in the course of solving the last subproblem, a convexity theory for general, nonlinear hybrid systems is developed, which can be easily extended for general, nonlinear hybrid systems. Subproblem (a) is the easiest to solve. A convexity theory is presented that allows convex relaxations of general, nonconvex Bolza type functions to be constructed for the optimization problem. This allows a deterministic branch-and-bound framework to be employed for the global optimization of the subproblem.
(cont.) Subproblems (b) and (c) are much more difficult to solve, and require the exploitation of structure. For subproblem (b), a hybrid superstructure is proposed that enables the linear structure to be retained. A branch-and-cut framework with a novel dynamic bounds tightening heuristic is proposed, and it is shown that the generation of cuts from dynamic bounds tightening can have a dramatic impact on the solution of the problem. For subproblem (c), a time transformation is employed to transform the problem into one with fixed transition times, but nonlinear dynamics. A convexity theory is developed for constructing convex relaxations of general, nonconvex Bolza type functions with the nonlinear hybrid system embedded, along with the development of bounding methods, based on the theory of differential inequalities. A novel bounding technique that exploits the time transformation is also introduced, which can provide much tighter bounds than that furnished utilizing differential inequalities.
by Cha Kun Lee.
Ph.D.
Pandit, Sanjay. "Texture segmentation by global optimization." Thesis, University of Surrey, 1999. http://epubs.surrey.ac.uk/843855/.
Full textLeclerc, Anthony P. "Efficient and reliable global optimization /." The Ohio State University, 1992. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487780393266914.
Full textViitanen, Sami. "Some new global optimization algorithms /." Åbo : Åbo Akad. Förl, 1997. http://www.gbv.de/dms/goettingen/239457927.pdf.
Full textMohd, Ismail Bin. "Global optimization using interval arithmetic." Thesis, University of St Andrews, 1987. http://hdl.handle.net/10023/13824.
Full textSchütze, Oliver. "Set oriented methods for global optimization." [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=976566982.
Full textBeckers, Markus [Verfasser]. "Toward global robust optimization / Markus Beckers." Aachen : Hochschulbibliothek der Rheinisch-Westfälischen Technischen Hochschule Aachen, 2014. http://d-nb.info/1059317370/34.
Full textHeath, Jeffrey W. "Global optimization of finite mixture models." College Park, Md. : University of Maryland, 2007. http://hdl.handle.net/1903/7179.
Full textThesis research directed by: Applied Mathematics and Scientific Computation Program. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
Wilson, Simon Paul. "Aircraft routing using nonlinear global optimization." Thesis, University of Hertfordshire, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.275117.
Full textSobester, A. "Enhancements to global design optimization techniques." Thesis, University of Southampton, 2003. https://eprints.soton.ac.uk/45904/.
Full textBashir, Hassan Abdullahi. "Global optimization with hybrid evolutionary computation." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/global-optimization-with-hybrid-evolutionary-computation(0392a891-dfae-4063-baf1-992cd0dc7df2).html.
Full textGreene, James J. "Global optimization of water distribution systems." Thesis, This resource online, 1992. http://scholar.lib.vt.edu/theses/available/etd-10062009-020212/.
Full textMonnet, Dominique. "Global minmax optimization for robust H∞ control." Thesis, Brest, École nationale supérieure de techniques avancées Bretagne, 2018. http://www.theses.fr/2018ENTA0009/document.
Full textH∞ control is nowadays used in many applications. This control technique enables to synthesize control laws which are robust with respect to external disturbances. Moreover, it allows to take model uncertainty into account in the synthesis process. As a consequence, H∞ control laws are robust with respect to both external disturbances and model uncertainty. A robust control law is a solution to an optimization problem, formulated from H∞ criteria. These criteria are the mathematical translations of the desired closed loop performance specifications. The two classical approaches to the optimization problem rely on the convex reformulation and local optimization methods. However, such approaches are unable to guarantee the optimality, with respect to the H∞ criteria, of the control law. This thesis proposes to investigate a global optimization approach to H∞ control. Contrary to convex and local approaches, global optimization methods enable to guarantee the optimality of the control, and also to take into account model uncertainty in a reliable way
Ahmed, Abdel-Rahman Hedar A. "Studies on metaheuristics continuous global optimization problems." 京都大学 (Kyoto University), 2004. http://hdl.handle.net/2433/145313.
Full textLančinskas, Algirdas. "Parallelization of random search global optimization algorithms." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2013. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2013~D_20130620_110438-17037.
Full textOptimizavimo uždaviniai sutinkami įvairiose mokslo ir pramonės srityse, tokiose kaip chemija, biologija, biomedicina, operacijų tyrimai ir pan. Paprastai efektyviausiai sprendžiami uždaviniai, turintys tam tikras savybes, tokias kaip tikslo funkcijų tiesiškumas, iškilumas, diferencijuojamumas ir pan. Tačiau ne visi praktikoje pasitaikantys optimizavimo uždaviniai tenkina šias savybes, o kartais iš vis negali būti išreiškiami adekvačia matematine išraiška. Tokiems uždaviniam spręsti yra populiarūs atsitiktinės paieškos optimizavimo metodai. Disertacijoje yra tiriami atsitiktinės paieškos optimizavimo metodai, jų lygiagretinimo galimybės ir taikymas praktikoje pasitaikantiems uždaviniams spręsti. Pagrindinis dėmesys skiriamas dalelių spiečiaus optimizavimo ir genetinių algoritmų modifikavimui ir lygiagretinimui. Disertacijoje yra siūloma dalelių spiečiaus optimizavimo algoritmo modifikacija, grįsta pieškos srities siaurinimu, ir tiriamos kelios algoritmo lygiagretinimo strategijos. Algoritmas yra taikomas erdvėlaivių skrydžių trajektorijų optimizavimo uždaviniui spręsti lygiagrečiųjų skaičiavimų sistemose. Taip pat yra siūlomas hibridinis globaliojo daugiakriterio optimizavimo algoritmas, gautas modifikuojant vieno agento stochastinės paieškos algoritmą ir įkomponuojant į daugiakriterio optimizavimo genetinį algoritmą. Siūlomos kelios daugiakriterio genetinio algoritmo lygiagretinimo strategijos. Jų pagrindu gauti lygiagretieji algoritmai eksperimentiškai tiriami sprendžiant... [toliau žr. visą tekstą]
Gutmann, H. M. "Radial basis function methods for global optimization." Thesis, University of Cambridge, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.599804.
Full textHenkle, Aimee L. (Aimee Leigh) 1975. "Global supply chain design and optimization methodology." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/34762.
Full textIncludes bibliographical references (p. 72).
The work for this thesis was performed at Honeywell in the Automation and Control Solutions (ACS) division. The project focuses on ACS's manufacturing strategy regarding its global supply chain design, primarily discussing the manufacturing growth opportunities available in emerging regions. Honeywell ACS's current methodology for the development of a long-term manufacturing strategy is based on growth and total cost reduction objectives. In order to comprehend the total cost of the manufacturing strategy, considerations such as inventory, logistics and duties, outsourcing and material sourcing are evaluated. The project also considers a factory's geographical location and ACS's year-by-year implementation plan. An outcome of this Honeywell project and the basis of this thesis is the development of a general supply chain design and optimization methodology that utilizes three analytical tools (Country Selection Framework, Total Cost Model and Implementation Plan Process) that are capable of validating the supply chain design of any company. The analytical tools can be used to verify key strategic supply chain decisions or to create a baseline manufacturing strategy. The following results can be determined using this supply chain design methodology: Determine an appropriate operating region for current or future business needs; Evaluate the feasibility of factory relocation projects by considering all relevant costs; Evaluate the cost implications of the supply chain structure by considering logistics, inventory and material sourcing costs; Understand the impact of outsourcing on the manufacturing strategy; Recommend a year-by-year implementation plan in the case of multiple projects and limited capital resources.
by Aimee L. Henkle.
S.M.
M.B.A.
Mayer, Theresia. "Efficient global optimization : analysis, generalizations and extensions." Thesis, University of Edinburgh, 2003. http://hdl.handle.net/1842/15297.
Full textStepanenko, Svetlana. "Global Optimization Methods based on Tabu Search." Doctoral thesis, kostenfrei, 2008. http://www.opus-bayern.de/uni-wuerzburg/volltexte/2008/3060/.
Full textIbraev, Suiunbek. "A new parallel method for verified global optimization." [S.l.] : [s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=963452304.
Full textSchutte, Jaco Francois. "Applications of parallel global optimization to mechanics problems." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0012932.
Full textPettersson, Tobias. "Global optimization methods for estimation of descriptive models." Thesis, Linköping University, Department of Electrical Engineering, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-11781.
Full textUsing mathematical models with the purpose to understand and store knowlegde about a system is not a new field in science with early contributions dated back to, e.g., Kepler’s laws of planetary motion.
The aim is to obtain such a comprehensive predictive and quantitative knowledge about a phenomenon so that mathematical expressions or models can be used to forecast every relevant detail about that phenomenon. Such models can be used for reducing pollutions from car engines; prevent aviation incidents; or developing new therapeutic drugs. Models used to forecast, or predict, the behavior of a system are refered to predictive models. For such, the estimation problem aims to find one model and is well known and can be handeled by using standard methods for global nonlinear optimization.
Descriptive models are used to obtain and store quantitative knowledge of system. Estimation of descriptive models has not been much described by the literature so far; instead the methods used for predictive models have beed applied. Rather than finding one particular model, the parameter estimation for descriptive models aims to find every model that contains descriptive information about the system. Thus, the parameter estimation problem for descriptive models can not be stated as a standard optimization problem.
The main objective for this thesis is to propose methods for estimation of descriptive models. This is made by using methods for nonlinear optimization including both new and existing theory.
Samuelsson, Oscar. "Benchmarking Global Optimization Algorithms for Core Prediction Identification." Thesis, Linköpings universitet, Reglerteknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-61253.
Full textLiberti, Leo Sergio. "Reformulation and convex relaxation techniques for global optimization." Thesis, Imperial College London, 2004. http://hdl.handle.net/10044/1/11807.
Full textDing, Baoyan. "A parametric solution for local and global optimization." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq21341.pdf.
Full textFowkes, Jaroslav Mrazek. "Bayesian numerical analysis : global optimization and other applications." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:ab268fe7-f757-459e-b1fe-a4a9083c1cba.
Full textAli, M. "Some modified stochastic global optimization algorithms with applications." Thesis, Loughborough University, 1994. https://dspace.lboro.ac.uk/2134/13429.
Full textHerrero-López, Sergio. "Large-scale simulator for global data infrastructure optimization." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/70759.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 165-172).
Companies depend on information systems to control their operations. During the last decade, Information Technology (IT) infrastructures have grown in scale and complexity. Any large company runs many enterprise applications that serve data to thousands of users which, in turn, consume this information in different locations concurrently and collaboratively. The understanding by the enterprise of its own systems is often limited. No one person in the organization has a complete picture of the way in which applications share and move data files between data centers. In this dissertation an IT infrastructure simulator is developed to evaluate the performance, availability and reliability of large-scale computer systems. The goal is to provide data center operators with a tool to understand the consequences of infrastructure updates. These alterations can include the deployment of new network topologies, hardware configurations or software applications. The simulator was constructed using a multilayered approach and was optimized for multicore scalability. The results produced by the simulator were validated against the real system of a Fortune 500 company. This work pioneers the simulation of large-scale IT infrastructures. It not only reproduces the behavior of data centers at a macroscopic scale, but allows operators to navigate down to the detail of individual elements, such as processors or network links. The combination of queueing networks representing hardware components with message sequences modeling enterprise software enabled reaching a scale and complexity not available in previous research in this area.
by Sergio Herrero-López.
Ph.D.
Daboul, Siad [Verfasser]. "Global Timing Optimization in Chip Design / Siad Daboul." Bonn : Universitäts- und Landesbibliothek Bonn, 2021. http://d-nb.info/1235525341/34.
Full textHirsch, Michael J. "Grasp-based heuristics for continuous global optimization problems." [Gainesville, Fla.] : University of Florida, 2006. http://purl.fcla.edu/fcla/etd/UFE0017140.
Full textZhou, Xiaojun. "Canonical Dual Algorithms for Global Optimization with Applications." Thesis, Federation University Australia, 2014. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/74678.
Full textPhD
Kunde, Christian [Verfasser]. "Global optimization in conceptual process design / Christian Kunde." Magdeburg : Universitätsbibliothek, 2017. http://d-nb.info/1135662266/34.
Full textRoddier, Nicolas. "Global optimization via neural networks and D.C. programming." Diss., The University of Arizona, 1994. http://hdl.handle.net/10150/186617.
Full textMcMeen, John Norman Jr. "Ranking Methods for Global Optimization of Molecular Structures." Digital Commons @ East Tennessee State University, 2014. https://dc.etsu.edu/etd/2447.
Full textMohammadi, Hossein. "Kriging-based black-box global optimization : analysis and new algorithms." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEM005/document.
Full textThe Efficient Global Optimization (EGO) is regarded as the state-of-the-art algorithm for global optimization of costly black-box functions. Nevertheless, the method has some difficulties such as the ill-conditioning of the GP covariance matrix and the slow convergence to the global optimum. The choice of the parameters of the GP is critical as it controls the functional family of surrogates used by EGO. The effect of different parameters on the performance of EGO needs further investigation. Finally, it is not clear that the way the GP is learned from data points in EGO is the most appropriate in the context of optimization. This work deals with the analysis and the treatment of these different issues. Firstly, this dissertation contributes to a better theoretical and practical understanding of the impact of regularization strategies on GPs and presents a new regularization approach based on distribution-wise GP. Moreover, practical guidelines for choosing a regularization strategy in GP regression are given. Secondly, a new optimization algorithm is introduced that combines EGO and CMA-ES which is a global but converging search. The new algorithm, called EGO-CMA, uses EGO for early exploration and then CMA-ES for final convergence. EGO-CMA improves the performance of both EGO and CMA-ES. Thirdly, the effect of GP parameters on the EGO performance is carefully analyzed. This analysis allows a deeper understanding of the influence of these parameters on the EGO iterates. Finally, a new self-adaptive EGO is presented. With the self-adaptive EGO, we introduce a novel approach for learning parameters directly from their contribution to the optimization
Andramonov, Mikhail. "Global minimization of some classes of generalized convex functions." Thesis, Federation University Australia, 2001. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/164850.
Full text