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Hybrid Model of Metaheuristic Algorithms
Šandera, Čeněk ; Zelinka, Ivan (referee) ; Matoušek, Radomil (referee) ; Šeda, Miloš (advisor)
The main topic of this PhD thesis is metaheuristic algorithm in wider scope. The first chapters are dedicated to a description of broader context of metaheuristics, i.e. various optimization classes, determination of their omplexity and different approaches to their solutions. The consequent discussion about metaheuristics and their typical characteristics is followed by several selected examples of metaheuristics concepts. The observed characteristics serve as a base for building general metaheuristics model which is suitable for developing brand new or hybrid algorithms. The thesis is concluded by illustration of author’s publications with discussion about their adaptation to the proposed model. On the attached CD, there is also available a program implementation of the created model.
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Test Optimization by Search-Based Algorithms
Starigazda, Michal ; Holík, Lukáš (referee) ; Letko, Zdeněk (advisor)
Testing of multi-threaded programs is a demanding work due to the many possible thread interleavings one should examine. The noise injection technique helps to increase the number of tested thread interleavings by noise injection to suitable program locations. This work optimizes meta-heuristics search techniques in the testing of concurrent programs by utilizing deterministic heuristic in the application of genetic algorithms in a space of legal program locations suitable for the noise injection. In this work, several novel deterministic noise injection heuristics without dependency on the random number generator are proposed in contrary to the most of currently used heuristic. The elimination of the randomness should make the search process more informed and provide better, more optimal, solutions thanks to increased stability in the results provided by novel heuristics. Finally, a benchmark of programs, used for the evaluation of novel noise injection heuristics is presented.
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Optimization by means of metaheuristics in Python using the DEAP library
Kesler, René ; Charvát, Pavel (referee) ; Klimeš, Lubomír (advisor)
{This thesis deals with optimization by means of metaheuristics, which are used for complicated engineering problems that cannot be solved by classical methods of mathematical programming. At the beginning, choosed metaheuristics are described: simulated annealing, particle swarm optimization and genetic algorithm; and then they are compared with use of test functions. These algorithms are implemented in Python programming language with use of package called DEAP, which is also described in this thesis. Algorithms are then applied for optimization of design parameters of the heat storage unit.
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Optimization by means of metaheuristics in Python using the DEAP library
Kesler, René ; Charvát, Pavel (referee) ; Klimeš, Lubomír (advisor)
{This thesis deals with optimization by means of metaheuristics, which are used for complicated engineering problems that cannot be solved by classical methods of mathematical programming. At the beginning, choosed metaheuristics are described: simulated annealing, particle swarm optimization and genetic algorithm; and then they are compared with use of test functions. These algorithms are implemented in Python programming language with use of package called DEAP, which is also described in this thesis. Algorithms are then applied for optimization of design parameters of the heat storage unit.
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Test Optimization by Search-Based Algorithms
Starigazda, Michal ; Holík, Lukáš (referee) ; Letko, Zdeněk (advisor)
Testing of multi-threaded programs is a demanding work due to the many possible thread interleavings one should examine. The noise injection technique helps to increase the number of tested thread interleavings by noise injection to suitable program locations. This work optimizes meta-heuristics search techniques in the testing of concurrent programs by utilizing deterministic heuristic in the application of genetic algorithms in a space of legal program locations suitable for the noise injection. In this work, several novel deterministic noise injection heuristics without dependency on the random number generator are proposed in contrary to the most of currently used heuristic. The elimination of the randomness should make the search process more informed and provide better, more optimal, solutions thanks to increased stability in the results provided by novel heuristics. Finally, a benchmark of programs, used for the evaluation of novel noise injection heuristics is presented.
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Hybrid Model of Metaheuristic Algorithms
Šandera, Čeněk ; Zelinka, Ivan (referee) ; Matoušek, Radomil (referee) ; Šeda, Miloš (advisor)
The main topic of this PhD thesis is metaheuristic algorithm in wider scope. The first chapters are dedicated to a description of broader context of metaheuristics, i.e. various optimization classes, determination of their omplexity and different approaches to their solutions. The consequent discussion about metaheuristics and their typical characteristics is followed by several selected examples of metaheuristics concepts. The observed characteristics serve as a base for building general metaheuristics model which is suitable for developing brand new or hybrid algorithms. The thesis is concluded by illustration of author’s publications with discussion about their adaptation to the proposed model. On the attached CD, there is also available a program implementation of the created model.
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Hybrid model of metaheuristic algorithms
Šandera, Čeněk ; Šeda, Miloš (advisor)
The main topic of this PhD thesis is metaheuristic algorithm in wider scope. The first chapters are dedicated to a description of broader context of metaheuristics, i.e. various optimization classes, determination of their omplexity and different approaches to their solutions. The consequent discussion about metaheuristics and their typical characteristics is followed by several selected examples of metaheuristics concepts. The observed characteristics serve as a base for building general metaheuristics model which is suitable for developing brand new or hybrid algorithms. The thesis is concluded by illustration of author’s publications with discussion about their adaptation to the proposed model. On the attached CD, there is also available a program implementation of the created model.
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Application of genetic algorithm for production scheduling of engineering company
Stariat, Jiří ; Skočdopolová, Veronika (advisor) ; Zouhar, Jan (referee)
This thesis is engaged in scheduling problem, his special types and methods of solving. Scheduling problem is a common operations research problem, which ranks among combinatorial problems. The aim of the scheduling problem is to assign certain activities and resources to individual time moments. Scheduling problem is NP-complete problem. Its computational complexity is thus so high, that there is currently no known algorithm that precisely solve its any instance in polynomial time. Is therefore used for its solution heuristics and metaheuristcs. In this thesis is described in detail metaheuristics of genetic algorithm. Application of genetic algorithm for production scheduling of specific engineering company is the main objective of this thesis.
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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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Rozvrhování výroby - teorie a praxe
Kašpar, Michal ; Pelikán, Jan (advisor) ; Kuncová, Martina (referee)
Manufactural activity is the basis of every sound economy. The risk for today's industrial establishments in our let us say european conditions is to hold competitiveness in the terms of global economy. This diploma thesis is focusing on solving problems of manufacturing scheduling with the view of theory and practice. It is impeach of real-life production. Scheduling belongs to hard combinatorial problems and therefore are usually solved by various heuristic or metaheuristic methods. For application of mentioned metaheuristic methods is important to use suitable choice of representative data.
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