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Implementation and Comparison of Nature-Inspired Search Algorithms
Malysák, Adam ; Husa, Jakub (referee) ; Sekanina, Lukáš (advisor)
This thesis deals with the description, implementation and comparison of genetic algorithm, genetic algorithm enhanced with local search heuristic and binary particle swarm optimization (BPSO). These are algorithms inspired by natural phenomena, specifically the evolution and movement of bird flocks or fish schools. Implemented algorithms are used to solve the 3-SAT problem, which is also described in this thesis. Algorithms are tested on 3-SAT benchmarks and compared to each other and to other papers.
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The travelling thief problem
Ternbach, Pavel ; Dosoudilová, Monika (referee) ; Kůdela, Jakub (advisor)
Recently, the field of algorithm optimization has been addressing a problem of large number of optimization problems not being nearly as complex as some real-world problems. These real-world problems are increasing in complexity, while the optimization problems are outdated. In order to understand and find better ways of solving these complex real-world problems, the travelling thief problem, also known by the acronym "TTP", was created. Travelling thief problem was designed to resemble real-world problems as closely as possible by combining two subproblems. Since solving the TTP is relatively difficult, various algorithms using different approaches have been developed. This thesis focuses on explaining and then comparing some of these algorithms.
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Evolutionary Optimization of Freight Transportation
Beránek, Michal ; Drahošová, Michaela (referee) ; Bidlo, Michal (advisor)
The following thesis deals with optimization of freight transport planning. The goal is to minimize expenses connected to transportation, which emerge from travelled distance. The expenses can be heavily reduced, if the routes are correctly planned, especially when there is a large number of customers to be served. This thesis focuses on solving the problem by using the evolutional algorithms, that are optimization methods based on principles of evolution. Thesis concentrates on Heterogeneous Fixed Fleet Vehicle Routing Problem. Thesis introduces multiple evolutional algorithms and their results are compared. The best algorithm, evolutional strategy with local neighbourhood search, achieves similar, for certain tasks even better results, than other existing evolutional algorithms, created to solve given problem.
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Graphic Animation of Problem Solving Methods
Macek, Jiří ; Jurka, Pavel (referee) ; Zbořil, František (advisor)
There are many kinds of implementation artificial intelligence for automatic solving problems by computer technology. The main topics of this bachelor's thesis are some typical methods, describing of their features, comparing them among and shows some useful techniques of algoritmization and implementation too. Main purpose of this thesis is creating application, which clearly demonstrates at chosen problems methods of their solving.
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Vehicle classification using inductive loops sensors
Halachkin, Aliaksei ; Klečka, Jan (referee) ; Honec, Peter (advisor)
This project is dedicated to the problem of vehicle classification using inductive loop sensors. We created the dataset that contains more than 11000 labeled inductive loop signatures collected at different times and from different parts of the world. Multiple classification methods and their optimizations were employed to the vehicle classification. Final model that combines K-nearest neighbors and logistic regression achieves 94\% accuracy on classification scheme with 9 classes. The vehicle classifier was implemented in C++.
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Evolutionary Optimization of Freight Transportation
Beránek, Michal ; Drahošová, Michaela (referee) ; Bidlo, Michal (advisor)
The following thesis deals with optimization of freight transport planning. The goal is to minimize expenses connected to transportation, which emerge from travelled distance. The expenses can be heavily reduced, if the routes are correctly planned, especially when there is a large number of customers to be served. This thesis focuses on solving the problem by using the evolutional algorithms, that are optimization methods based on principles of evolution. Thesis concentrates on Heterogeneous Fixed Fleet Vehicle Routing Problem. Thesis introduces multiple evolutional algorithms and their results are compared. The best algorithm, evolutional strategy with local neighbourhood search, achieves similar, for certain tasks even better results, than other existing evolutional algorithms, created to solve given problem.
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Vehicle classification using inductive loops sensors
Halachkin, Aliaksei ; Klečka, Jan (referee) ; Honec, Peter (advisor)
This project is dedicated to the problem of vehicle classification using inductive loop sensors. We created the dataset that contains more than 11000 labeled inductive loop signatures collected at different times and from different parts of the world. Multiple classification methods and their optimizations were employed to the vehicle classification. Final model that combines K-nearest neighbors and logistic regression achieves 94\% accuracy on classification scheme with 9 classes. The vehicle classifier was implemented in C++.
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Graphic Animation of Problem Solving Methods
Macek, Jiří ; Jurka, Pavel (referee) ; Zbořil, František (advisor)
There are many kinds of implementation artificial intelligence for automatic solving problems by computer technology. The main topics of this bachelor's thesis are some typical methods, describing of their features, comparing them among and shows some useful techniques of algoritmization and implementation too. Main purpose of this thesis is creating application, which clearly demonstrates at chosen problems methods of their solving.
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Acceleration of Discrete Optimization Heuristics Using GPU
Pecháček, Václav ; Jaroš, Jiří (referee) ; Pospíchal, Petr (advisor)
Thesis deals with discrete optimization problems. It focusses on faster ways to find good solutions by means of heuristics and parallel processing. Based on ant colony optimization (ACO) algorithm coupled with k-optimization local search approach, it aims at massively parallel computing on graphics processors provided by Nvidia CUDA platform. Well-known travelling salesman problem (TSP) is used as a case study. Solution is based on dividing task into subproblems using tour-based partitioning, parallel processing of distinct parts and their consecutive recombination. Provided parallel code can perform computation more than seventeen times faster than the sequential version.
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Local on-line marketing
Šimůnková, Tereza ; Sedláček, Jiří (advisor) ; Nový, Marek (referee)
This Master thesis is devoted to the local marketing on-line of the small and medium businesses (SMEs). The aim of this thesis is to provide SMEs with suggestions for local marketing on the internet with respect to their limited budget. The outputs of the survey which deals with local search show how users search for services (products) on-line and how they use a location in the search queries. The results appearing on the first page (SERP) were analyzed with special focus on the listings from Google Places. Such listings can significantly influence the businesses visibility and conversion rate on-line.
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