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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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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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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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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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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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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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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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Particle Swarm Optimization Library
Hruban, Milan ; Strnadel, Josef (referee) ; Bidlo, Michal (advisor)
The aim of this thesis is to develop a library that is able to solve optimization tasks using PSO. The library is implemented using Kotlin and is designed to achieve high extensibility and usability. Moreover, a tool for processing and statistical analysis of experiments performed using the library is implemented by means of the Jupyter Notebook environment. The utilization of these tools creates a setup suitable for experimenting with the current and developing new variations of the PSO algorithm.
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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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Swarm Intelligence
Winklerová, Zdenka ; Šaloun, Petr (referee) ; Škrinárová,, Jarmila (referee) ; Zbořil, František (advisor)
The intention of the dissertation is the applied research of the collective ( group ) ( swarm ) intelligence . To demonstrate the applicability of the collective intelligence, the Particle Swarm Optimization ( PSO ) algorithm has been studied in which the problem of the collective intelligence is transferred to mathematical optimization in which the particle swarm searches for a global optimum within the defined problem space, and the searching is controlled according to the pre-defined objective function which represents the solved problem. A new search strategy has been designed and experimentally tested in which the particles continuously adjust their behaviour according to the characteristics of the problem space, and it has been experimentally discovered how the impact of the objective function representing a solved problem manifests itself in the behaviour of the particles. The results of the experiments with the proposed search strategy have been compared to the results of the experiments with the reference version of the PSO algorithm. Experiments have shown that the classical reference solution, where the only condition is a stable trajectory along which the particle moves in the problem space, and where the influence of a control objective function is ultimately eliminated, may fail, and that the dynamic stability of the trajectory of the particle itself is not an indicator of the searching ability nor the convergence of the algorithm to the true global solution of the solved problem. A search strategy solution has been proposed in which the PSO algorithm regulates its stability by continuous adjustment of the particles behaviour to the characteristics of the problem space. The proposed algorithm influenced the evolution of the searching of the problem space, so that the probability of the successful problem solution increased.
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