Master thesis is focused on optimization of cogeneration system for purpose of rating optimization methods and evaluating properties of these methods. For each method there is description together with block schemes. First part of thesis is devoted to description of methods and their comparison. Second part consists of development of hybrid algorithm, which is used to optimize cogeneration systém model. Each algorithm compared is together with hybrid algorithms included in annexes.

This thesis deals with optimization problems with main focus on logistic Vehicle Routing Problem (VRP). In the first part term optimization is established and most important optimization problems are presented. Next section deals with methods, which are capable of solving those problems. Furthermore it is explored how to apply those methods to specific VRP, along with presenting some enhancement of those algorithms. This thesis also introduces learning method capable of using knowledge of previous solutions. At the end of the paper, experiments are performed to tune the parameters of used algorithms and to discuss benefit of suggested improvements.

This master's thesis is about nature optimalization technigues. Evolution algortihms together with main thesis topic, Particle Swarm Optimization, is introduced in the following chapter. Car setup and simulator TORCS are introduced in next chapter. Design and implementation are introduced in next chapters. Destination of t master's thesis is finding optimal car setups for different curcuits.

Disertační práce začíná rešerší výpočtů pro návrh sendvičových nosníků, desek a složitějších konstrukcí, kde zaujímá významnou roli MKP. Dále jsou popsány optimalizační metody pro ujasnění široké oblasti matematického programování a základních principů topologické optimalizace až po její implementaci na kompozitní konstrukce jinými autory. Pro názornost jsou zmíněny jak analytické, tak i numerické přístupy k optimalizaci sendvičů, kde numerické přístupy umožňují řešit daleko širší oblast úkolů. Cíl disertační práce je stanoven jako implementace zautomatizovaného algoritmu pro optimalizaci za účelem vylepšení návrhového procesu sendvičů s ohledem na napjatost a únosnost. Cíle je dosaženo prostřednictvím vlastní implementace gradientní optimalizace založené na principech topologické optimalizace, známé jako diskrétní optimalizace materiálu (Discrete Material Optimization - DMO) a jejích variant, které pomáhají najít optimální vrstvení. Přístup k materiálové interpolaci a interpolaci poruchový omezujících podmínek je vyvinut a naprogramován v pythonu za použití teorie smykových deformací prvního řádu (First Order Shear Deformation Theory - FSDT) pro vyhodnocení napětí na elementech na základě zatížení daného MKP řešičem Nastran. Gradientní optimalizér hledá nejlepší materiály pro každou vrstvu potahu sendviče a jádra z definovaných kandidátů. Program je odzkoušený na příkladech různé složitosti od nosníku tvořeného jedním elementem, kde je skutečné optimum známé, až po praktickou úlohu sendvičové kuchyňky z dopravního letadla. Výsledky ukázaly, že algoritmus je schopen dosáhnout diskrétního řešení bez (významného) narušení omezujících podmínek a může tedy být prakticky využit ke zefektivnění koncepčního návrhu sendvičů.

Evolutionary algorithms are optimization techniques inspired by the actual evolution of biological species. They use conceptually simple process of two repeating phases of reproduction and fitness-based selection, that iteratively evolves each time better solutions. Evolutionary algorithms receive a lot of attention for being able to solve very hard optimization problems, where other optimization techniques might fail due to existence of many local optima. Wide range of different variants of evolutionary algorithms have been proposed. In this thesis, we will focus on the area of Estimation of Distribution Algorithms (EDA). When creating the next generation, EDAs transform the selected high-fitness population into a probability distribution. New generation is obtained by sampling the estimated distribution. We will design and and implement combinations of existing EDAs that will operate in business-specific environment, that can be characterized as tree-like structure of both discrete and continuous variables. Also, additional linear inequality constraints are specified to applicable solutions. Implemented application communicates with provided interfaces, retrieving the problem model specification and storing populations into database. Database is used to assign externally computed fitness values from...

The goal of this thesis is to create a tool for an optimization of code for the task-based parallel framework called Bobox. The optimizer tool reduces a number of short and long running tasks based on a static code analysis. Some cases of short-running tasks cause an unnecessary scheduling overhead. The Bobox scheduler can schedule a task even though the task does not have all input data. Unless, the scheduler has enough information not to schedule such task. In order to remove such short-running task, the tool analyses its input usage and informs the scheduler. Long-running tasks inhibit a parallel execution in some cases. A bigger task granularity can significantly improve execution times in a parallel environment. In order to remove a long-running task, the tool has to be able to evaluate a runtime code complexity and yield a task execution in the appropriate place. Powered by TCPDF (www.tcpdf.org)

Robust Investment Portfolios Zdeněk Konfršt Abstract This master's thesis pursues the construction of stable, robust and growth portfo- lios in active portfolio management. These portfolios provide limited downside risks, short-time drawdowns and substantial growth prospects. We argue that the construc- tion of such portfolios is based on security selection as well as on portfolio theory (the Mean-Variance Model, MVM). The equity based portfolios were constructed and tested on real market data from the 1995-2014 period. The tested portfolios outperformed the S&P 500 out of and within the risk-reward ratio domain. Robust portfolios build on the MVM but they are less sensitive to errors of param- eters estimation. We experimented with several robust approaches and the results confirmed that the robust portfolios were less sensitive to outliers, less volatile and more stable (robust). The bottom-up process of security selection seems time consuming and labor intensive. Therefore we proposed an alternative approach to select stocks with so- called "cluster analysis". Generally, the cluster analysis classifies similar objects (companies) into similar groups. Technical and fundamental parameters of companies provided needed classification parameters. The classification was run over companies from the German DAX...

Digital river network dataset is an important source of information in any aspect of water management decision making. It is also a base for modelling or scientific research in many different fields. Development of the dataset in the Czech Republic had been fragmented in a past and as a result three different datasets have been developed that cover the whole of the state's territory. The datasets contain different geometries, different and often conflicting attributes and serve different purposes. Today the time has come that water management decision makers have realised that the situation is no longer sustainable and make effort to merge the datasets into one. The task brings in several technical issues and a potential for severe legal consequences. The aim of this study is to develop a methodological approach to merging the existing datasets into one. This methodological approach to decision which of the conflicting or different attributes should be adopted is based on assumption that the existing datasets will be merged into one consisting the best of all. Comparison of features in the existing dataset will inevitably lead to many conflicts when it will be necessary to decide which of the considered features should be adopted to the resulting dataset. The study considers the main purposes which...

Evolutionary algorithms are one of the most successful methods for solving non-traditional optimization problems. As they employ only function values of the objective function, evolutionary algorithms converge much more slowly than optimization methods for smooth functions. This property of evolutionary algorithms is particularly disadvantageous in the context of costly and time-consuming empirical way of obtaining values of the objective function. However, evolutionary algorithms can be substantially speeded up by employing a sufficiently accurate regression model of the empirical objective function. This thesis provides a survey of utilizability of regression trees and their ensembles as a surrogate model to accelerate convergence of evolutionary optimization.

In the present work, stochastic network models representing a project as a set of activities are studied, as well as different approaches to these models. The critical path method, stochastic network models with probability constraints, finding a reference project duration, worst-case analysis in stochastic networks and optimization of the parameters of the probability distributions of the activity durations are studied. Use of stochastic network models in telecommunications networks is also briefly presented. In a numerical study, some of these models are implemented and the~related numerical results are analyzed.