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Acceleration of Particle Swarm Optimization Using GPUs
Krézek, Vladimír ; Schwarz, Josef (referee) ; Jaroš, Jiří (advisor)
This work deals with the PSO technique (Particle Swarm Optimization), which is capable to solve complex problems. This technique can be used for solving complex combinatorial problems (the traveling salesman problem, the tasks of knapsack), design of integrated circuits and antennas, in fields such as biomedicine, robotics, artificial intelligence or finance. Although the PSO algorithm is very efficient, the time required to seek out appropriate solutions for real problems often makes the task intractable. The goal of this work is to accelerate the execution time of this algorithm by the usage of Graphics processors (GPU), which offers higher computing potential while preserving the favorable price and size. The boolean satisfiability problem (SAT) was chosen to verify and benchmark the implementation. As the SAT problem belongs to the class of the NP-complete problems, any reduction of the solution time may broaden the class of tractable problems and bring us new interesting knowledge.
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Spectrum sensing and multicarrier systems optimization for cognitive radio
Povalač, Karel ; Šimša, Jan (referee) ; Galajda,, Pavol (referee) ; Maršálek, Roman (advisor)
The doctoral thesis deals with spectrum sensing and subsequent use of the frequency spectrum by multicarrier communication system, which parameters are set on the basis of the optimization technique. Adaptation settings can be made with respect to several requirements as well as state and occupancy of individual communication channels. The system, which is characterized above is often referred as cognitive radio. Equipments operating on cognitive radio principles will be widely used in the near future, because of frequency spectrum limitation. One of the main contributions of the work is the novel usage of the Kolmogorov – Smirnov statistical test as an alternative detection of primary user signal presence. The new fitness function for Particle Swarm Optimization (PSO) has been introduced and the Error Vector Magnitude (EVM) parameter has been used in the adaptive greedy algorithm and PSO optimization. The dissertation thesis also incorporates information about the reliability of the frequency spectrum sensing in the modified greedy algorithm. The proposed methods are verified by the simulations and the frequency domain energy detection is implemented on the development board with FPGA.
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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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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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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 EMI filter Circuitry
Surovec, Vojtěch ; Soldán, Josef (referee) ; Dřínovský, Jiří (advisor)
In introduction of work, I presented the state of the art of measuring insertion loss of the EMI filter and the description of individual methods of measuring is also included. After that I dealt with global optimization methods and some of them I theoretically described. Mainly PSO method was studied carefully, and the principle of optimization in details are presented in the thesis, because this method was used for optimization of the filters parameters in the thesis. Next part of work deals with theoretical and mathematical expression of insertion loss of the EMI filters. Due to the mathematical expression of the insertion loss, the values of parasitic elements of given filter have been optimized in different measuring systems. This optimization was applied on synthetic data. By this step the functionality of optimization was proved and also the fitness function has been tuned and optimised. The last part of thesis works with optimization on measured data of given filters. I found out inaccuracy in mathematical expression of given model of EMI filter, therefore it was necessary to come up with the new filter model. The accuracy increases by using the new filter model. In next step the new model was enlarged by new spurious components.
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Portfolio Optimization Using Metaheuristics
Haviar, Martin ; Doubravský, Karel (referee) ; Budík, Jan (advisor)
This thesis deals with design and implementation of an investment model, which applies methods of Post-modern portfolio theory. Particle swarm optimization (PSO) metaheuristic was used for portfolio optimization and the parameters were analyzed with several experiments. Johnsons SU distribution was used for estimation of future returns as it proved to be the best of analyzed distributions. The result is software application written in Python, which is tested for stability and performance of model in extreme situations.
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