Tato práce popisuje simulátor TORCS a optimalizační algoritmy, jenž jsou využívány při tvorbě autonomních řidičů pro tento simulátor. Hlavním cílem je navržení nového autonomního řidiče, který se bude schopen s použitím přírodou inspirovaných optimalizačních technik vyrovnat již dříve navrženým řešením. Chování implementovaného řešení lze rozdělit do dvou hlavních částí, které jsou využívány v různých rozdílných etapách závodu. Zahřívací kolo je využito pro vytvoření modelu trati, ze kterého je posléze získána optimální trajektorie pomocí genetického algoritmu. Této trajektorie je potom využíváno v samotné kvalifikaci či závodě pro zajetí co nejrychlejšího kola. Z důvodu složitosti problému optimalizace celé trajektorie je nutno tuto trajektorii rozdělit na menší úseky nazývané segmenty, přičemž každý z nich je potom optimalizován odděleně. Jednotlivé optimalizované segmenty jsou následně spojeny dohromady, aby opět utvořily trajektorii pro celou trať. Protože některé přechody mezi segmenty mohou být nesouvislé, je zde znovu aplikován genetický algoritmus pro jejich vyhlazení. Během závodu je tato trajektorie následována, přičemž se z ní odvíjí i maximální možná rychlost v daném úseku. V práci jsme ukázali, že vzorkování trati s následnou optimalizací pomocí genetického algoritmu trvá pouze zlomek času vyhrazeného pro zahřívací kolo. Nejen díky tomuto se řešení jeví jako vhodné pro závody autonomních řidičů a může být dále rozšířeno.

The presented dissertation deals with the problem of deploying the charging infrastructure for electric vehicles in the Czech Republic. The core of the thesis is a mathematical optimization model, which is implemented in the language of MATLAB computing software. The model consists of several sub-units representing separate models of studied sub-problems. The individual chapters of the work describe successively these sub models. The sub models are: demand model of the charging service, model of charging supply, charging simulator model, optimization model and its resolving optimization method. The optimization model is accelerated by parallelization on the graphics card. The optimization method is designed as a case-specific implementation of genetic algorithms on a population of tree-structured individuals. The final chapter deals with an economic aspect of the problem under consideration, the implications of the findings and the role that the optimization model plays in the context under consideration. The main benefit of the work lies in the formulation of the problem as a mathematical model, the accompanying analyses and the provided justifications. Any user with updated data can then use this work along with the attached scripts to find answers to questions about the relationship between electromobility and the charging infrastructure.

This thesis focuses on prices, quality & risks of the Facility management. In the theoretical part there are explained points referring to the Facility management, it’s contributions, ways of securing them, important fields and it’s targets. Also individual levels of cooperation and the job description of the management. There is also a mention of an important standardization, of which the Facility management couldn’t function without. Also the example of service contract and lastly, a very important chapter is dedicated to the quality of work provision and Facility management services. The practical part of this thesis is the case study based on a questionnaire of how to support services of the two different subjects, hotels and construction companies. The case study also describes the cost of these services, ways of controlling the quality provided and contracts with appointed workers. The result of this thesis is a detailed description of potential risks which can appear in the individual type of services. There is a suggested optimization for all the risks mentioned.

Damaging changes and interventions in the water cycle in our landscape caused mainly in the last century together with uncertainties from climate change are the cause of more frequent occurrences of hydrological extremes. In Hydrology, the most urgent problem is that the values of the long-term mean flows are decreasing in rivers as well as the yield of groundwater sources, but on the other hand, we cannot forget to the problem of extreme floods. In these consequences developing methods and tools to uncertainty analysis of the reservoir yield and of the reservoir flood protection is very important, useful and desired. The main aim was to determine the functional volumes of the reservoir considering input data measurement uncertainties and to quantify them and was explained how uncertainty took into account in results. The active storage capacity was determined from the historical series of monthly flows that were affected by uncertainties, next were applied on water evaporation, seepage losses of the dam and morphological volume-area curves. The simulation-optimization reservoir model was developed and temporal reliability as reservoir yield performance measures was applied. This model will extend the existing UNCE_RESERVOIR software. The flood capacity was determined from random flood wave variations were obtained by repeatedly generating uncertainty on the flood hydrograph. Software was developed based on the modified Klemes method, which was able to transform flood waves. The measurement uncertainties of data inputs were created using Monte Carlo method in both softwares. By connecting two softwares, the functional volumes of the reservoir under conditions of measurement uncertainties were complexly determined. The case study was applied to the real water reservoir, in the Morava River Basin. The result will be whether the dam is resistant to the current conditions, or the optimal design of the functional volumes of reservoir under conditions uncertainties.

This master´s thesis deals with the registration of ultrasound sequences using evolutionary algorithms. The theoretical part of the thesis describes the process of image registration and its optimalization using genetic and metaheuristic algorithms. The thesis also presents problems that may occur during the registration of ultrasonographic images and various approaches to their registration. In the practical part of the work, several optimization methods for the registration of a number of sequences were implemented and compared.

The article describes how to produce energy necessary for sensor supply. These generators are used as a local source operating on vibratory principle. Mechanical vibrations that occur in moving machines, in nature etc. are used in order to gain required energy. So are kinesis principles and analysis employed in some of the avaible generators. It also contains patent and literary background research. Work is oriented to solving mechatronic perimeter, in the concrete micro - generator. Mechatronic perimeter piles from seat power control parts and mechanical parts, where common solving equation system leads to correct solving. Mentioned analyses be of consequence for usage optimization methods artificial intelligence. Optimization method are used on optimum solving proposal micro - generator. To inquest dynamism system was used program Simulink (part of MATLAB), generator is buckthorn for f = 17 [Hz] acceleration yam = 0,5g [ms-2]. As a algorithm is used SOMA – All to one.

The Master’s thesis deals with production scheduling in an industrial company. It uses the means of artificial intelligence to develop an appropriate production schedule in a generalized Flow-shop Programming problem. This problem can be solved by application which is a result of this thesis and was prepaired with use of the software Matlab 7.1 and its Genetic Algorithm and Direct Search toolbox. There is a part devoted to the use of advanced production systems (APS) and the concept of the operative production planning in praxis as well. The thesis pays attention to various optimization models in production scheduling and supply chain management too.

This thesis presents the reader the importance of optimization and probabilistic assessment of structures for civil engineering problems. Chapter 2 further investigates the combination between previously proposed optimization techniques and probabilistic assessment in the form of optimization constraints. Academic software has been developed for the purposes of demonstrating the effectiveness of the suggested methods and their statistical testing. 3th chapter summarizes the results of testing previously described optimization method (called Aimed Multilevel Sampling), including a comparison with other optimization techniques. In the final part of the thesis, described procedures have been demonstrated on the selected optimization and reliability problems. The methods described in text represents engineering approach to optimization problems and aims to introduce a simple and transparent optimization algorithm, which could serve to the practical engineering purposes.

The thesis deals the issue of solution of continuous systems by evolutionary computational techniques. Evolutionary computing techniques fall into the field of softcomputing, an advanced metaheuristics optimization that is becoming more and more a method of solving complicated optimization problems with the gradual increase in computing performance of computers. The solution of continuous systems, or the synthesis of continuous control circuits, is one of the areas where these advanced algorithms find their application. When dealing with continuous systems we will focus on regulatory issues. Evolutionary computing can then become a tool not only for optimization of controller parameters but also to design its structure. Various algorithms (genetic algorithm, differential evolution, etc.) can be used to optimize the parameters of the controller, for the design of the controller structurewe usually encounter so called grammatical evolution. However, the use of grammatical evolution is not necessary if appropriate coding is used, as suggested in the presented thesis. The thesis presents a method of designing the structure and parameters of a general linear controller using the genetic algorithm. A general linear regulator is known also as so called polynomial controller, if we encounter the polynomial theory of control. The method of encoding the description of the general linear controller into the genetic chain is crucial, it determines a set of algorithms that are usable for optimization and influence the efficiency of the calculations. Described coding, effective EVT implementation, including multi-criteria optimization, is a key benefit of this work.