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Traveling Salesman Problem
Šůstek, Martin ; Snášelová, Petra (referee) ; Zbořil, František (advisor)
This thesis is focused on modification of known principles ACO and GA to increase their performance. Thesis includes two new principles to solve TSP. One of them can be used as an initial population generator. The appendix contains the implementation of the application in Java. The description of this application is also part of the thesis. One part is devoted to optimization in order to make methods more efficient and produce shorter paths. In the end of the thesis are described experiments and their results with different number of places from 101 up to 3891.
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Mathematical models for transportation problems
Votavová, Helena ; Novotný, Jan (referee) ; Popela, Pavel (advisor)
The thesis deals with modelling and solution techniques for the selected transportation problems. Firstly, historical remarks and application-related comments are introduced. Then the selected transportation problems are defined and mathematical programming and graph theory concepts are utilised to model them. The travelling salesman problem and suitable algorithms are under focus. The original implementation in GAMS and Python is discussed. Algorithms have been tested for the instance based on the set of 73 towns in the Czech Republic. Finally, the test results are evaluated and compared.
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Evolutionary algorithms
Haupt, Daniel ; Polách, Petr (referee) ; Honzík, Petr (advisor)
The first part of this work deals with the optimization and evolutionary algorithms which are used as a tool to solve complex optimization problems. The discussed algorithms are Differential Evolution, Genetic Algorithm, Simulated Annealing and deterministic non-evolutionary algorithm Taboo Search.. Consequently the discussion is held on the issue of testing the optimization algorithms through the use of the test function gallery and comparison solution all algorithms on Travelling salesman problem. In the second part of this work all above mentioned optimization algorithms are tested on 11 test functions and on three models of placement cities in Travelling salesman problem. Firstly, the experiments are carried out with unlimited number of accesses to the fitness function and secondly with limited number of accesses to the fitness function. All the data are processed statistically and graphically.
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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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Travelling Salesman Problem
Kolář, Adam ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
The aim of this bachelor's thesis is to design a testing environment for the traveling salesman problem and compare the effectiveness of different approaches to the solution. The first part discussed the possibility of genetic algorithms, depending on the setting of a crossover, mutations and population size. In the second part, there is the same problem using two types of neural networks. The representative of the self-learning net was chosen Kohonen neural network. Hopfield neural network represents a method of minimizing the energy function with fixed coefficients. At both neural networks, there were described possible advantages and disadvantages. In the end, all the findings were interpreted in a global context.
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Travelling Salesman Problem Application in Particular Logistics Enterprise
Ružička, Vladimír ; Bartík, Vladimír (referee) ; Kreslíková, Jitka (advisor)
This paper deals with optimal distribution issues. One may find listed problems of real life linked to distribution. Moreover, there are explained travelling salesman problem, vehicle routing problem and its variants. This work brings an overview of different ways how to solve vehicle routing problem. In practical part, there is an analysis of distribution of real company. The concept of application is presented in the second part of this paper. This concept could reduce costs of distribution in analyzed company. Testing is aimed mainly on the variant VRPCL (Vehicle Routing Problem with Continuos Loading).
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Computational tasks for solving parallel data processing
Rexa, Denis ; Uher, Václav (referee) ; Mašek, Jan (advisor)
The goal of this diploma thesis was to create four laboratory exercises for the subject "Parallel Data Processing", where students will try on the options and capabilities of Apache Spark as a parallel computing platform. The work also includes basic setup and use of Apache Kafka technology and NoSQL Apache Cassandra database. The other two lab assignments focus on working with a Travelling Salesman Problem. The first lab was designed to demonstrate the difficulty of a task where the student will face an exponential increase in complexity. The second task consists of an optimization algorithm to solve the problem in cluster. This algorithm is subjected to performance measurements in clusters. The conclusion of the thesis contains recommendations for optimization as well as comparison of running with different number of computing devices.
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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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Ant Colony Optimization for Solving Big Instances of TSP
Ramosová, Patrícia ; Jaroš, Jiří (referee) ; Bidlo, Michal (advisor)
Currently, many applications place emphasis on finding the optimal solution to a particular problem. However, it is typical for some tasks that their complexity increases exponentially depending on the size of the instance. A typical example of such a problem is the Traveling Salesman Problem (TSP). One class of methods that have proven to be very helpful in solving TSPs are ant algorithms. Nonetheless, they reached their limit - a high number of cities in the instance and became almost unusable due to time and memory requirements. This bachelor thesis aims to modify the ant algorithm and create a system capable of quickly and efficiently solve large-scale TSPs without significant loss in the quality of the solution found. Optimization will focus on reducing memory complexity and total execution time.
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