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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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Solving Optimization Tasks by ACO Algorithms
Habrnál, Matěj ; Samek, Jan (referee) ; Zbořil, František (advisor)
The presented thesis puts its main focus on the basic optimization algorithms ACO (Ant Colony Optimization) and their development and seeks the inspiration in the ants live. The aim is to demonstrate the activity of these algorithms on optimization problems - the traveling salesman problem and the finding food sources problem and optimal routes between an anthill and food. The thesis also describes experiments that try to determine the influence of adjustable parameters of ant algorithms. First, ACO algorithms theory is described followed then by the application of these algorithms on both selected optimization problems. The conclusion sums up experiments analysis with established applications and evaluating prospective results.
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Experiments with the Swarm Intelligence
Hula, Tomáš ; Zbořil, František (referee) ; Grulich, Lukáš (advisor)
This work deals with the issue of swarm intelligence as a subdiscipline of artificial intelligence. It describes biological background of the dilemma briefly and presents the principles of searching paths in ant colonies as well. There is also adduced combinatorial optimization and two selected tasks are defined in detail: Travelling Salesman Problem and Quadratic Assignment Problem. The main part of this work consists of description of swarm intelligence methods for solving mentioned problems and evaluation of experiments that were made on these methods. There were tested Ant System, Ant Colony System, Hybrid Ant System and Max-Min Ant System algorithm. Within the work there were also designed and tested my own method Genetic Ant System which enriches the basic Ant System i.a. with development of unit parameters based on genetical principles. The results of described methods were compared together with the ones of classical artificial intelligence within the frame of both solved problems.
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Heuristic Solving of Planning Problems
Novotná, Kateřina ; Křena, Bohuslav (referee) ; Letko, Zdeněk (advisor)
This thesis deals with the implementation of the metaheuristic algorithms into the Drools Planner. The Drools Planner is an open source tool for solving optimization problems. This work describes design and implementation of Ant colony optimization metaheuristics in the Drools Planner. Evaluation of the algorithm results is done by Drools Planner benchmark with different kinds of optimization problems.
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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.
|
|
|
Solving Optimization Tasks by ACO Algorithms
Habrnál, Matěj ; Samek, Jan (referee) ; Zbořil, František (advisor)
The presented thesis puts its main focus on the basic optimization algorithms ACO (Ant Colony Optimization) and their development and seeks the inspiration in the ants live. The aim is to demonstrate the activity of these algorithms on optimization problems - the traveling salesman problem and the finding food sources problem and optimal routes between an anthill and food. The thesis also describes experiments that try to determine the influence of adjustable parameters of ant algorithms. First, ACO algorithms theory is described followed then by the application of these algorithms on both selected optimization problems. The conclusion sums up experiments analysis with established applications and evaluating prospective results.
|
|
|
Heuristic Solving of Planning Problems
Novotná, Kateřina ; Křena, Bohuslav (referee) ; Letko, Zdeněk (advisor)
This thesis deals with the implementation of the metaheuristic algorithms into the Drools Planner. The Drools Planner is an open source tool for solving optimization problems. This work describes design and implementation of Ant colony optimization metaheuristics in the Drools Planner. Evaluation of the algorithm results is done by Drools Planner benchmark with different kinds of optimization problems.
|
|
|
Experiments with the Swarm Intelligence
Hula, Tomáš ; Zbořil, František (referee) ; Grulich, Lukáš (advisor)
This work deals with the issue of swarm intelligence as a subdiscipline of artificial intelligence. It describes biological background of the dilemma briefly and presents the principles of searching paths in ant colonies as well. There is also adduced combinatorial optimization and two selected tasks are defined in detail: Travelling Salesman Problem and Quadratic Assignment Problem. The main part of this work consists of description of swarm intelligence methods for solving mentioned problems and evaluation of experiments that were made on these methods. There were tested Ant System, Ant Colony System, Hybrid Ant System and Max-Min Ant System algorithm. Within the work there were also designed and tested my own method Genetic Ant System which enriches the basic Ant System i.a. with development of unit parameters based on genetical principles. The results of described methods were compared together with the ones of classical artificial intelligence within the frame of both solved problems.
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