The main topic of this diploma thesis is a design of the PIFA antenna working in GSM bands. In the beginning there is a brief analysis of planar antennas. The thesis describes PIFA antenna and the techniques for minimization of dimensions of the antenna. Essential part of the thesis is dedicated to multicriterial optimalizaton of the antenna shape. The genetic algorithm programmed in the MATLAB enviroment cooperates with a full-wave solver CST to obrain desired impedance matching of the antenna its radiationt paterns. Also dimensions of the antenna can be minimized using the optimization procedure. Final part of the thesis compares measured data of the optimalized antenna with results obtained in CST Microwave Studio.

This thesis studies the effectiveness of bio-inspired optimization algorithms in controlling adaptive antenna arrays. Smart antennas are able to automatically extract the desired signal from interferer signals and external noise. The angular pattern depends on the number of antenna elements, their geometrical arrangement, and their relative amplitude and phases. In the present work different antenna geometries are tested and compared when their array weights are optimized by different techniques. First, the Genetic Algorithm and Particle Swarm Optimization algorithms are used to find the best set of phases between antenna elements to obtain a desired antenna pattern. This pattern must meet several restraints, for example: Maximizing the power of the main lobe at a desired direction while keeping nulls towards interferers. A series of experiments show that the PSO achieves better and more consistent radiation patterns than the GA in terms of the total area of the antenna pattern. A second set of experiments use the Signal-to-Interference-plus-Noise-Ratio as the fitness function of optimization algorithms to find the array weights that configure a rectangular array. The results suggest an advantage in performance by reducing the number of iterations taken by the PSO, thus lowering the computational cost. During the development of this thesis, it was found that the initial states and particular parameters of the optimization algorithms affected their overall outcome. The third part of this work deals with the meta-optimization of these parameters to achieve the best results independently from particular initial parameters. Four algorithms were studied: Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing and Hill Climb. It was found that the meta-optimization algorithms Local Unimodal Sampling and Pattern Search performed better to set the initial parameters and obtain the best performance of the bio-inspired methods studied.

As estruturas de concreto pré-moldado tendem a ser mais moduladas e mais padronizados do que as estruturas de concreto moldadas no local, logo as técnicas de otimização podem produzir mais benefícios econômicos devido à produção em escala. Entre as técnicas de otimização utilizadas em engenharia estrutural, os algoritmos genéticos têm sido reconhecidos como uma forte tendência devido à sua facilidade de implementação e os excelentes resultados obtidos. Este trabalho trata da otimização integrada de pavimentos de edifícios com estruturas de concreto pré-moldado utilizando algoritmos genéticos e minimizando os custos. O principal objetivo é apresentar uma formulação para a otimização do pavimento, baseado em restrições arquitetônicas; restrições estruturais e restrições construtivas. A função-objetivo contemplou não só o consumo de materiais, mas também os aspectos relativos à fabricação, transporte e montagem. Atesta-se a consistência da representação do problema pelo modelo em função dos resultados que foram muito coerentes com a prática dos projetos. Os vários exemplos apresentados mostraram a robustez e a aplicabilidade do modelo e evidenciou-se a possibilidade de sua utilização em um sistema de apoio à tomada de decisão, que sirva como ferramenta de auxílio aos projetistas na concepção estrutural. Foi implementada a rotina dos transgênicos, que melhorou a convergência, e, a dos gêmeos, que aumentou a variabilidade da população. / The precast concrete structures are more modular and standardized than the cast in place concrete structures, therefore optimization techniques can improve economics gain because of series production. Among the optimization techniques in structural engineering design, genetic algorithms have been recognized as a trend. This work aims the floor precast concrete building optimization using GA\'s and minimizing the cost. The main goal of the work is to present a model to optimize the floor taking account of the structural, architectonics and constructive restrictions. The adopted model reached its purpose of the representing the more realist as possible the problem. The cost function considered not only the material consumption but the manufacture, transport and assembled stage. An integrated structural optimization is performed from the structural layout (columns position, directions and spans for beams and hollow cores) through the complete elements detailing (dimensions and reinforcement). The example results evidence the effectiveness of the formulation, they were very consistent with the design practice and they present the system application possibility like a decision support system that helps the engineer in the projects development. It was implemented a transgenic routine to improve the convergence and a twin routine to improve the variability of the population.

The results section of my thesis includes three chapters. The first two chapters are on theoretical game theory. In both chapters, by mathematical modelling and game theoretical tools, I am predicting the behaviour of the players in some real world issues. Hoteling-Downs model plays an important role in the modern political interpretations. The first chapter of this study investigates an extension of Hoteling-Downs model to have multi-dimensional strategy space and asymmetric candidates. Chapter 3 looks into the inspection game where the inspections are not the same in the series of sequential inspections. By modelling the game as a series of recursive zero-sum games I find the optimal strategy of the players in the equilibrium. The forth chapter investigates direct optimization methods for large scale problems. Using Matlab implementations of Genetic and Nelder-Mead algorithms, I compare the efficiency and accuracy of the most famous direct optimization methods for unconstraint optimization problems based on differing number of variables.

As estruturas de concreto pré-moldado tendem a ser mais moduladas e mais padronizados do que as estruturas de concreto moldadas no local, logo as técnicas de otimização podem produzir mais benefícios econômicos devido à produção em escala. Entre as técnicas de otimização utilizadas em engenharia estrutural, os algoritmos genéticos têm sido reconhecidos como uma forte tendência devido à sua facilidade de implementação e os excelentes resultados obtidos. Este trabalho trata da otimização integrada de pavimentos de edifícios com estruturas de concreto pré-moldado utilizando algoritmos genéticos e minimizando os custos. O principal objetivo é apresentar uma formulação para a otimização do pavimento, baseado em restrições arquitetônicas; restrições estruturais e restrições construtivas. A função-objetivo contemplou não só o consumo de materiais, mas também os aspectos relativos à fabricação, transporte e montagem. Atesta-se a consistência da representação do problema pelo modelo em função dos resultados que foram muito coerentes com a prática dos projetos. Os vários exemplos apresentados mostraram a robustez e a aplicabilidade do modelo e evidenciou-se a possibilidade de sua utilização em um sistema de apoio à tomada de decisão, que sirva como ferramenta de auxílio aos projetistas na concepção estrutural. Foi implementada a rotina dos transgênicos, que melhorou a convergência, e, a dos gêmeos, que aumentou a variabilidade da população. / The precast concrete structures are more modular and standardized than the cast in place concrete structures, therefore optimization techniques can improve economics gain because of series production. Among the optimization techniques in structural engineering design, genetic algorithms have been recognized as a trend. This work aims the floor precast concrete building optimization using GA\'s and minimizing the cost. The main goal of the work is to present a model to optimize the floor taking account of the structural, architectonics and constructive restrictions. The adopted model reached its purpose of the representing the more realist as possible the problem. The cost function considered not only the material consumption but the manufacture, transport and assembled stage. An integrated structural optimization is performed from the structural layout (columns position, directions and spans for beams and hollow cores) through the complete elements detailing (dimensions and reinforcement). The example results evidence the effectiveness of the formulation, they were very consistent with the design practice and they present the system application possibility like a decision support system that helps the engineer in the projects development. It was implemented a transgenic routine to improve the convergence and a twin routine to improve the variability of the population.

The work deals with the optimization of trusses construction building and transport machinery. The goal was to create an algorithm that can design an optimized design. The simulation took place on two experiments involving 52 sets of different entries, which are processed in detail into graphs. One-dimensional target mass or price function is used as part of optimization, but there is also an incorporated multidimensional purpose function. The finite element variation method for the beam system is used for the strength calculation of the truss structure and the genetic algorithm is used for optimization. At the end of the work, specific steps are formulated that lead to the most appropriate algorithm settings.

Car following (CF) behaviour modelling has made significant progress in both traffic engi-neering and traffic psychology during recent decades. Autonomous vehicles (AVs) have been demonstrated to optimise traffic flow and increase traffic stability. Consequently, sever-al car-following models have been proposed based on various car following criteria, leading to a range of model parameter sets. In traffic engineering, Intelligent Driving Model (IDM) are commonly used as microscopic traffic flow models to simulate a single vehicle's behav-iour on a road. Observational data can be employed to parameter calibrate IDM models, which enhances their practicality for real-world applications. As a result, the calibration of model parameters is crucial in traffic simulation research and typically involves solving an optimization problem. Within the given context, the Nelder-Mead（NM）algorithm, particle swarm optimization (PSO) algorithm and genetic algorithm (GA) are utilized in this study for parameterizing the IDM model, using abundant trajectory data from five different road conditions. The study further examines the effects of various algorithms on the IDM model in different road sections, providing useful insights for traffic simulation and optimization.:Table of Contents CHAPTER 1 INTRODUCTION 1 1.1 BACKGROUND AND MOTIVATION 1 1.2 STRUCTURE OF THE WORK 3 CHAPTER 2 BACKGROUND AND RELATED WORK 4 2.1 CAR-FOLLOWING MODELS 4 2.1.1 General Motors model and Gazis-Herman-Rothery model 5 2.1.2 Optimal velocity model and extended models 6 2.1.3 Safety distance or collision avoidance models 7 2.1.4 Physiology-psychology models 8 2.1.5 Intelligent Driver model 10 2.2 CALIBRATION OF CAR-FOLLOWING MODEL 12 2.2.1 Statistical Methods 13 2.2.2 Optimization Algorithms 14 2.3 TRAJECTORY DATA 21 2.3.1 Requirements of Experimental Data 22 2.3.2 Data Collection Techniques 22 2.3.3 Collected Experimental Data 24 CHAPTER 3 EXPERIMENTS AND RESULTS 28 3.1 CALIBRATION PROCESS 28 3.1.1 Objective Function 29 3.1.2 Errors Analysis 30 3.2 SOFTWARE AND METHODOLOGY 30 3.3 NM RESULTS 30 3.4 PSO RESULTS 37 3.4.1 PSO Calibrator 37 3.4.2 PSO Results 44 3.5 GA RESULTS 51 3.6 OPTIMIZATION PERFORMANCE ANALYSIS 58 CHAPTER 4 CONCLUSION 60 REFERENCES 62