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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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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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Artificial Life Models
Ďuričeková, Daniela ; Martinek, David (referee) ; Peringer, Petr (advisor)
This bachelor thesis describes design and implementation of an artificial life simulator. The work is divided into four parts. The aim of the first part is to provide a brief overview of artificial life and related terminology. The second part deals with selected design patterns and the process of designing a simulation system, whose purpose is to simulate an ecosystem of artificial life entities. The subsequent part focuses on implementation of individual system components. Finally, the system is tested and evaluated on two sample models.
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PSO-Particle Swarm Optimizations
Veselý, Filip ; Jaroš, Jiří (referee) ; Schwarz, Josef (advisor)
This work deals with swarm intelligence, strictly speaking particle swarm intelligence. It shortly describes questions of optimization and some optimization techniques. Part of this work is recherché of variants of particle swarm optimization algorithm. These algorithms are mathematically described. Their advantages or disadvantages in comparison with the basic PSO algorithm are mentioned. The second part of this work describes mQPSO algorithm and created modification mQPSOPC. Described algorithms are compared with each other and with another evolution algorithm on several tests.
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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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Robot path planning by means of ant algorithms
Pěnčík, Martin ; Krček, Petr (referee) ; Dvořák, Jiří (advisor)
This thesis deals with robot path planning. It contains an overview of general approaches for path planning and describes methods of swarm intelligence and their application for robot path planning. This paper also contains proposals of adjustments for ant algorithms and it presents experimental results of algorithm implementation.
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Robot path planning by means of swarm intelligence
Schimitzek, Aleš ; Krček, Petr (referee) ; Dvořák, Jiří (advisor)
This diploma thesis deals with the path planning by swarm intelligence. In the theoretical part it describes the best known methods of swarm intelligence (Ant Colony Optimization, Bee Swarm Optimization, Firefly Swarm Optimization and Particle Swarm Optimization) and their application for path planning. In the practical part particle swarm optimization is selected for the design and implementation of path planning in the C#.
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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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Swarm Intelligence in MRDS
Kučera, Lukáš ; Hynčica, Ondřej (referee) ; Honzík, Petr (advisor)
The background research in this Master’s thesis is focused on swarm intelligence. Further, there are two experiments described. They are based on released publications and they study behaviour of a group of robots during a puck gathering and during a target search. The actual thesis follows a repetition of these experiments in Microsoft Robotics Developer Studio (RDS), a free robotics simulation environment. The realization of both experiments in RDS is documented in detail and the achieved results are evaluated and compared with the results described in the publications. In conclusion, the thesis summarizes basic features, advantages and disadvantages of developing in RDS, based on a personal experience.
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