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Toolbox for the cooperation of MATLAB and external simulation programs
Moravec, Petr ; Sekanina, Lukáš (referee) ; Oliva, Lukáš (advisor)
In this Master's thesis scripting interface of two programs CST Microwave studio and Ansoft HFSS for the purpose of analysis of electromagnetic structures is described. The work is focuses control of these programs with help of scripting languages and system's interface of MS Windows XP. Next the process of connecting programs with MATLAB is shown on commented scripts together with an example of complete analysis of a chosen problem, and the import and export of results results in MATLAB. Further the functions which form programming interface between MATLAB and simulation programs are designed and implemented. The interconnection layer makes the complete control of simulating programs possible using the function description published in the official documentation of used simulation programs. The layer is described in reference manual in detail and it is used for optimization with use of Particle swarm optimalization (PSO) of planar antenna model. Then there is presented another usage of the layer for an implementation of global optimization methods - SOMA and DE including suggestion of process for comparison efficiency of optimization algorithms on simple electromagnetic models.
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The Application of PSO in Business
Veselý, Filip ; Kaštovský, Petr (referee) ; Dostál, Petr (advisor)
This work deals with two optimization problems, traveling salesman problem and cluster analysis. Solution of these optimization problems are applied on INVEA-TECH company needs. It shortly describes questions of optimization and some optimization techniques. Closely deals with swarm intelligence, strictly speaking particle swarm intelligence. Part of this work is recherché of variants of particle swarm optimization algorithm. The second part describes PSO algorithms solving clustering problem and traveling salesman problem and their implementation in Matlab language.
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Ladění parametrů LQG řízení pomocí evolučních metod
Marton, Filip ; Brablc, Martin (referee) ; Grepl, Robert (advisor)
This master thesis deals with the design of evolutionary algorithms to optimize LQG control of two laboratory models. It also deals with the creation of simplified models used to describe real models and with the subsequent estimation of their parameters. After this section it deals with optimization of control parameters with the use of simplified models, testing the control on laboratory models and summarizing the results of controlling the models.
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PSO-algorithms and possibilities for their use in cryptanalysis.
Svetlíková, Lenka ; Tůma, Jiří (advisor) ; Hojsík, Michal (referee)
The aim of the thesis was to investigate the usage of PSO algorithm in the area of cryptanalysis. We applied PSO to the problem of simple substitution and to DES attack. By a modified version of PSO algorithm we achieved better or comparable results as by the usage of other biologically motivated algorithms. We suggested a method how to use PSO to attack DES and we were able to break it with the knowledge of only 20 plain texts and corresponding cipher texts. We have analyzed the reasons of failure to break more than a 4 rounds of DES and provided explanation for it. At the end we described the basic principles of differential cryptanalysis for DES and presented a specific mo- dification of PSO for searching optimal differential characteristics for DES. For simple ciphers, PSO is working efficiently but for sophisticated ciphers like DES, without in- corporating deep internal knowledge about the process into the algorithm, we could not expect significant outcomes. 1
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PSO-algorithms and possibilities for their use in cryptanalysis.
Svetlíková, Lenka ; Tůma, Jiří (advisor) ; Hojsík, Michal (referee)
The aim of the thesis was to investigate the usage of PSO algorithm in the area of cryptanalysis. We applied PSO to the problem of simple substitution and to DES attack. By a modified version of PSO algorithm we achieved better or comparable results as by the usage of other biologically motivated algorithms. We suggested a method how to use PSO to attack DES and we were able to break it with the knowledge of only 20 plain texts and corresponding cipher texts. We have analyzed the reasons of failure to break more than a 4 rounds of DES and provided explanation for it. At the end we described the basic principles of differential cryptanalysis for DES and presented a specific mo- dification of PSO for searching optimal differential characteristics for DES. For simple ciphers, PSO is working efficiently but for sophisticated ciphers like DES, without in- corporating deep internal knowledge about the process into the algorithm, we could not expect significant outcomes. 1
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Application of optimization methods in hydrological modeling
Jakubcová, Michala ; Máca, Petr (advisor) ; Hanel, Martin (referee)
Finding the optimal state of reality is the main purpose of the optimization process. The best variant from many possibilities is selected, and the effectiveness of the given system increases. Optimization has been applied in many real life engineering problems as in hydrological modelling. Within the hydrological case studies, the optimization process serves to estimate the best set of model parameters, or to train model weights in artificial neural networks.
Particle swarm optimization (PSO) is relatively recent optimization technique, which has only a few parameters to adjust, and is easy to implement to the selected problem. The original algorithm was modified by many authors. They
focused on changing the initialization of particles in the swarm, updating the population topology, adding new parameters into the equation, or incorporating shuffling mechanism into the algorithm.
The modifications of PSO algorithm improve the performance of the optimization, prevent the premature convergence, and decrease computation time. Therefore, the main aims of the presented doctoral thesis consist of proposal of a new PSO modification with its implementation in C++ programming language. More PSO variants were compared and analysed, and the best methods based on benchmark problems were applied in two hydrological case studies.
The first case study focused on utilization of PSO algorithms in inverse problem related to estimation of parameters of rainfall-runoff model Bilan. In the second case study, combination of artificial neural networks with PSO methods was introduced for forecasting the Standardized precipitation evapotranspiration drought index.
It was found out, that particle swarm optimization is a suitable tool for solving problems in hydrological modelling. The most effective PSO modifications are the one with adaptive version of parameter of inertia weight, which updates the velocity of particles during searching through the multidimensional space via feedback information. The shuffling mechanism and redistribution of particles into complexes, at which the PSO runs separately, also significantly improve the performance.
The contribution of this doctoral thesis lies in creation of new PSO modification, which was tested on benchmark problems, and was successfully applied in two hydrological case studies. The results of this thesis also extended the utilization of PSO methods in real life engineering optimization problems. All analysed PSO algorithms are available for later use within other research projects.
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Solving Optimization Tasks by PSO Algorithms
González, Marek ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
In this document we describe the Particle Swarm Optimization (PSO) and discuss its performance in solving optimization tasks. PSO is stochastic population-based computational method mainly focused on continuous optimization. We give an introduction to the field of optimization and provide a theoretical description of the PSO method. We have implemented the method in C/C++ and investigated the best working parameter set. The implementation is evaluated on clustering, travelling salesman problem, and function minimization case studies.
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Optimization of a Racing Car Setup within TORCS Simulator
Srnec, Pavel ; Jaroš, Jiří (referee) ; Pospíchal, Petr (advisor)
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.
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Particle Swarm Optimization on GPUs
Záň, Drahoslav ; Petrlík, Jiří (referee) ; Jaroš, Jiří (advisor)
This thesis deals with a population based stochastic optimization technique PSO (Particle Swarm Optimization) and its acceleration. This simple, but very effective technique is designed for solving difficult multidimensional problems in a wide range of applications. The aim of this work is to develop a parallel implementation of this algorithm with an emphasis on acceleration of finding a solution. For this purpose, a graphics card (GPU) providing massive performance was chosen. To evaluate the benefits of the proposed implementation, a CPU and GPU implementation were created for solving a problem derived from the known NP-hard Knapsack problem. The GPU application shows 5 times average and almost 10 times the maximum speedup of computation compared to an optimized CPU application, which it is based on.
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