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Analysis of Various Approaches to Solving Optimization Tasks
Knoflíček, Jakub ; Samek, Jan (referee) ; Zbořil, František (advisor)
This paper deals with various approaches to solving optimization tasks. In prolog some examples from real life that show the application of optimization methods are given. Then term optimization task is defined and introducing of term fitness function which is common to all optimization methods follows. After that approaches by particle swarm optimization, ant colony optimization, simulated annealing, genetic algorithms and reinforcement learning are theoretically discussed. For testing we are using two discrete (multiple knapsack problem and set cover problem) and two continuous tasks (searching for global minimum of Ackley's and Rastrigin's function) which are presented in next chapter. Description of implementation details follows. For example description of solution representation or how current solutions are changed. Finally, results of measurements are presented. They show optimal settings for parameters of given optimization methods considering test tasks. In the end are given test tasks, which will be used for finding optimal settings of given approaches.
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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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Solving of Optimisation Tasks Inspired by Living Organisms
Popek, Miloš ; Peringer, Petr (referee) ; Martinek, David (advisor)
We meet with solving of optimization problems every day, when we try to do our tasks in the best way. An Ant Colony Optimization is an algorithm inspired by behavior of ants seeking a source of food. The Ant Colony Optimization is successfuly using on optimization tasks, on which is not possible to use a classical optimization methods. A Genetic Algorithm is inspired by transmision of a genetic information during crossover. The Genetic Algorithm is used for solving optimization tasks like the ACO algorithm. The result of my master's thesis is created simulator for solving choosen optimization tasks by the ACO algorithm and the Genetic Algorithm and a comparison of gained results on implemented tasks.
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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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Metrics and Criteria for Socio-Technical System Diagnostic
Raudenská, Lenka ; Dohnal, Mirko (referee) ; Nenadál, Jaroslav (referee) ; Fiala, Alois (advisor)
This doctoral thesis is focused on metrics and the criteria for socio-technical system diagnostics, which is a high profile topic for companies wanting to ensure the best in product quality. More and more customers are requiring suppliers to prove reliability in the production and supply quality of products according to given specifications. Consequently the ability to produce quality goods corresponding to customer requirements has become a fundamental condition in order to remain competitive. The thesis firstly lays out the basic strategies and rules which are prerequisite for a successful working company in order to ensure provision of quality goods at competitive costs. Next, methods and tools for planning are discussed. Planning is important in its impact on budget, time schedules, and necessary sourcing quantification. Risk analysis is also included to help define preventative actions, and reduce the probability of error and potential breakdown of the entire company. The next part of the thesis deals with optimisation problems, which are solved by Swarm based optimisation. Algorithms and their utilisation in industry are described, in particular the Vehicle routing problem and Travelling salesman problem, used as tools for solving specialist problems within manufacturing corporations. The final part of the thesis deals with Qualitative modelling, where solutions can be achieved with less exact quantitative information of the surveyed model. The text includes qualitative algebra descriptions, which discern only three possible values – positive, constant and negative, which are sufficient in the demonstration of trends. The results can also be conveniently represented using graph theory tools.
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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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Ant colony optimization of antennas on EBG substrates
Wilder, Roman ; Lukeš, Zbyněk (referee) ; Horák, Jiří (advisor)
This diploma thesis deals with optimization of planar antennas on the Electromagnetic Bandgap (EBG) substrates by the help of Ant Colony Optimization (ACO). This method is based on the communications mechanisms of a real ant colony. Firstly, the working principle of the planar antennas and the theory of the Ant Colony Optimization are analyzed. Next, the description of the working principle of the Electromagnetic Bandgap and generally physical phenomena accompanying electromagnetic waves propagation in a periodic medium are given. In the second part of this thesis, the ACO was implemented into the VBA language, and was applied to two models of planar antennas. These models were created in the CST Microwave Studio. After an optimization of the antennas the results were evaluated, and the optimization of one of the antennas was compared to the optimization methods in CST Microwave Studio. Then, the standard substrate of the second model was replaced by the EBG substrate, and the results were confronted. Two types of EBG lattice were used. The design procedure of the square lattice was described, and the dispersion diagram was created in the CST Microwave Studio. In the final part of thesis, the verification of the results was carried out in Ansoft HFSS, and the results from both simulation programs were compared to each other.
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Ant Colony Optimization: Implementace a testování biologicky inspirované optimalizační metody
Havlík, Michal
Havlík, M. Ant Colony Optimization: Implementation and testing of bio-inspired optimization method. Diploma thesis. Brno, 2015. This thesis deals with the implementation and testing of algorithm Ant Colony Optimization as a representative of the family of bio-inspired opti-mization methods. A given algorithm is described, analyzed and subsequently put into context with the problems which can be solved. Based on the collec-ted information is designed implementation that solves the Traveling sale-sman problem. Implementation contains graphical user interface to track the algorithm. Implementation is further optimized using parallel programming and other methods. Finally the implementation compared and summarized results.
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Solving the combinatorial optimization problems with the Ant Colony Optimization metaheuristic method
Chu, Andrej ; Jablonský, Josef (advisor) ; Janáček, Jaroslav (referee) ; Linda, Bohdan (referee)
The Ant Colony Optimization belongs into the metaheuristic methods category and it has been developing quite recently. So far it has shown its capabalities to over-perform other metaheuristic methods in quality of the solutions. This work brings analysis of the possible applications of the method on the classical optimization combinatorial problems -- traveling salesman problem, vehicle routing problem, knapsack problem, generalized assignment problem and maximal clique problem. It also deals with the practical experiments with application on several optimization problems and analysis of the time and memory complexity of such algorithms. The last part of the work is dedicated to the possibility of parallelization of the algorithm, which was result of the application of the ACO method on the traveling salesman problem. It brings analysis of the crucial operations and data synchronization issues, as well as practical example and demonstration of the parallelized version of the algorithm.
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