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Applications of Ant Algorithms
Kaščák, Imrich ; Vašíček, Zdeněk (referee) ; Bidlo, Michal (advisor)
The presented thesis focuses on the basic optimization algorithm of Ant Colony Optimization (ACO) - Ant System (AS) and its extension, Ant Colony System (ACS) on Traveling Salesman Problem (TSP). The essence of these algorithms is to find the optimal solution (the shortest path) in a specified instance containing several locations. The thesis demonstrates verification of behaviour of both algorithms, experimental study of impact of adjustable parameters of ant algorithms on result. Furthermore, the thesis is focused on examining the idea of optimizing detection of image edges by introducing a modification into an existing solution. Modified solution experiments are performed and compared to the original.
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Genetic Algorithms
Masárová, Mária ; Zbořil, František (referee) ; Zbořil, František (advisor)
This thesis deals with genetic algorithm, their terminology and use. It describes various problems that can be solved by using genetic algorithms. Different algorithms of swarm intelligence are also presented in this thesis, while firefly algorithm also serves to compare the efficiency between it and genetic algorithm. The main task of this thesis is to perform experiments with three optimization tasks, namely, travelling salesman problem, boolean satisfiability problem and searching for extreme in function.
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Optimization Algorithms Inspired by Nature
Babjarčiková, Lenka ; Zbořil, František (referee) ; Zbořil, František (advisor)
This thesis deals with four optimization algorithms inspired by nature. It describes ant colony optimization algorithm, marriage in honeybees optimization algorithm, grey wolf optimization algorithm and simulated annealing algorithm. The main part of this thesis is the application of these algorithms for solving three optimization problems. One of the problems is travelling salesman problem, which is solved by ant colony optimization, next problem is searching for extreme of function solved by grey wolf optimization and simulated annealing algorithms and the last is boolean satisfiability problem solved by marriage in honeybees optimization algorithm. Thesis contains experiments with these algorithms and reviews gained results.
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Optimization Algorithms Inspired by Nature
Babjarčiková, Lenka ; Zbořil, František (referee) ; Zbořil, František (advisor)
This thesis deals with four optimization algorithms inspired by nature. It describes ant colony optimization algorithm, marriage in honeybees optimization algorithm, grey wolf optimization algorithm and simulated annealing algorithm. The main part of this thesis is the application of these algorithms for solving three optimization problems. One of the problems is travelling salesman problem, which is solved by ant colony optimization, next problem is searching for extreme of function solved by grey wolf optimization and simulated annealing algorithms and the last is boolean satisfiability problem solved by marriage in honeybees optimization algorithm. Thesis contains experiments with these algorithms and reviews gained results.
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Ant colony optimization
Kovács, Peter ; Pangrác, Ondřej (advisor) ; Balko, Martin (referee)
The aim of this work was to compare metaheuristic Ant Colony with other metaheuristics like Simulated Annealing, Tabu Search or Greedy algo- rithms. Metaheuristics were compared on three different NP-hard problems. Specificaly, Traveling Salesman Problem, Graph Coloring Problem and Set Covering Problem. Detailed description of implementation of metaheuristics in particular problems can be found in this work. Metaheuristics were com- pared based on cost function and execution time. However, this work tries to identify instances, in which Ant Colony was the best choice. Ant Colony is reliable metaheuristic on large Traveling Salesman or Set Covering problems.
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Genetic Algorithms
Masárová, Mária ; Zbořil, František (referee) ; Zbořil, František (advisor)
This thesis deals with genetic algorithm, their terminology and use. It describes various problems that can be solved by using genetic algorithms. Different algorithms of swarm intelligence are also presented in this thesis, while firefly algorithm also serves to compare the efficiency between it and genetic algorithm. The main task of this thesis is to perform experiments with three optimization tasks, namely, travelling salesman problem, boolean satisfiability problem and searching for extreme in function.
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Applications of Ant Algorithms
Kaščák, Imrich ; Vašíček, Zdeněk (referee) ; Bidlo, Michal (advisor)
The presented thesis focuses on the basic optimization algorithm of Ant Colony Optimization (ACO) - Ant System (AS) and its extension, Ant Colony System (ACS) on Traveling Salesman Problem (TSP). The essence of these algorithms is to find the optimal solution (the shortest path) in a specified instance containing several locations. The thesis demonstrates verification of behaviour of both algorithms, experimental study of impact of adjustable parameters of ant algorithms on result. Furthermore, the thesis is focused on examining the idea of optimizing detection of image edges by introducing a modification into an existing solution. Modified solution experiments are performed and compared to the original.
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