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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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Proposal of prediction model sales of selected food commodities
Řešetková, Dagmar ; Dostál, Petr (referee) ; Krčmarský, Miroslav (referee) ; Zelinka, Ivan (referee) ; Rais, Karel (advisor)
The dissertation is generally focused on the use of artificial intelligence tools in practice and with regard to the focus of study in the field of Management and Business Economics at using the tools of artificial intelligence in corporate practice, as a tool for decision support at the operational and tactical level management. In the narrower sense, the task deals with the proposal of the prediction sales model of selected food commodities. The proposed model is designed to serve as a substitute for a human expert in support decision-making process in the purchase of selected commodities, especially when training new staff and extend the currently used methods of managerial decision-making about artificial intelligence tools for company management and existing employees. The aim of this dissertation is the design prediction sales model of selected food commodities (apples and potatoes) for specific wholesale of fruit and vegetable operating in the Czech Republic. To become familiar with the behaviour of selected commodities were used primary and secondary research as well and knowledge gained from Czech and foreign literature sources and research. The resulting predictive model is developed using statistical analysis of time series and the sales prediction proceeds using the tools of artificial intelligence and is modeled by an artificial neural network. The dissertation in the practical part also contains proposals for the use of the prediction model and partial processing procedures for: • practice, • theory, • pedagogical activities.
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Optimalization of Constructional Teams Creation by Genetic Algorithms
Špaček, Jiří ; Zelinka, Ivan (referee) ; Šeda, Miloš (referee) ; Ošmera, Pavel (advisor)
The thesis pertains to optimisation of workgroups in companies. It is based on the work of Dr. Meredith Belbin from the Henley Management College, who is the author of the so-called Belbin’s team role theory. The theory defines fundamental roles within a team including specifications of the behavioural patterns while stipulating that in order to ensure proper functionality of a team, it is essential for all the roles to be represented in it. However, in practice it is necessary for specific people to comply not only with certain personal and psychological requirements but also professional expertise and other requirements. Nevertheless, by the means of adding these parameters to specific people, an enormous number of possible alternatives of the resulting team, which may not be evaluated (easily and in the real time) using traditional methods, proves to come to existence. Therefore, the so-called genetic algorithms inspired by natural development processes originally described by J. G. Mendel and Ch. Darwin were selected for evaluation purposes. The genetic algorithms feature good solutions to the task to be resolved in a very short time while the task does not have to be based on exact specifications and therefore several solutions might exist. A Java application was created within the scope of the thesis; its core comprises a genetic algorithm and it was used for the purpose of modelling of specific teams. The results provided by the application were subsequently verified by the means of creation of teams used for completion of new tasks and monitoring their activities in practice. Furthermore, the model verification of teams previously created solely on the basis of experience of executives was performed and the respective results were compared.
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Aerofoil Aerodynamic Features Optimization
Müller, Jan ; Rozehnal,, Dalibor (referee) ; Popela, Robert (referee) ; Zelinka, Ivan (referee) ; Ošmera, Pavel (advisor)
The content of the presented thesis is advanced optimization of the aerofoil wing of a general aircraft. Advanced metaheuristic optimization techniques based on evolutionary calculations and swarm algorithms are used for optimization. These algorithms are characterized by robustness of optimization and engineered degree of convergence and optimality of the solution. Within the solution, fundamental modifications of the original aerofoil optimizations were designed and implemented. A new variant of aerofoil evolutionary algorithms (aEA) was created from the original evolutionary algorithm (EA), followed by a new variant of aerofoil particle swarm optimization (aPSO) developed from the original particle swarm optimization (PSO). Then the hybridization of the mentioned methods was created in a parallel variant. The Bezier-PARSEC 3434 parameterization model that generates the aerofoil shape was used for the optimization process. A parametric model based on B-Spline was used to optimize the original aerofoil. Fluid dynamics simulation for the calculation of basic aerodynamic features (lift, drag, moment) was realized by Xfoil software. The results are then verified using fluid dynamics simulation (CFD ANSYS Fluent). From the point of view of optimization tasks developed by optimization and implementation, it is clear that this is a complex interdisciplinary task, the results of which are presented in this thesis.
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Aerofoil Aerodynamic Features Optimization
Müller, Jan ; Popela, Robert (referee) ; Zelinka, Ivan (referee) ; Rozehnal,, Dalibor (referee) ; Ošmera, Pavel (advisor)
The content of the presented thesis is advanced optimization of the aerofoil wing of a general aircraft. Advanced metaheuristic optimization techniques based on evolutionary calculations and swarm algorithms are used for optimization. These algorithms are characterized by robustness of optimization and engineered degree of convergence and optimality of the solution. Within the solution, fundamental modifications of the original aerofoil optimizations were designed and implemented. A new variant of aerofoil evolutionary algorithms (aEA) was created from the original evolutionary algorithm (EA), followed by a new variant of aerofoil particle swarm optimization (aPSO) developed from the original particle swarm optimization (PSO). Then the hybridization of the mentioned methods was created in a parallel variant. The Bezier-PARSEC 3434 parameterization model that generates the aerofoil shape was used for the optimization process. A parametric model based on B-Spline was used to optimize the original aerofoil. Fluid dynamics simulation for the calculation of basic aerodynamic features (lift, drag, moment) was realized by Xfoil software. The results are then verified using fluid dynamics simulation (CFD ANSYS Fluent). From the point of view of optimization tasks developed by optimization and implementation, it is clear that this is a complex interdisciplinary task, the results of which are presented in this thesis.
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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.
|
|
|
Optimalization of Constructional Teams Creation by Genetic Algorithms
Špaček, Jiří ; Zelinka, Ivan (referee) ; Šeda, Miloš (referee) ; Ošmera, Pavel (advisor)
The thesis pertains to optimisation of workgroups in companies. It is based on the work of Dr. Meredith Belbin from the Henley Management College, who is the author of the so-called Belbin’s team role theory. The theory defines fundamental roles within a team including specifications of the behavioural patterns while stipulating that in order to ensure proper functionality of a team, it is essential for all the roles to be represented in it. However, in practice it is necessary for specific people to comply not only with certain personal and psychological requirements but also professional expertise and other requirements. Nevertheless, by the means of adding these parameters to specific people, an enormous number of possible alternatives of the resulting team, which may not be evaluated (easily and in the real time) using traditional methods, proves to come to existence. Therefore, the so-called genetic algorithms inspired by natural development processes originally described by J. G. Mendel and Ch. Darwin were selected for evaluation purposes. The genetic algorithms feature good solutions to the task to be resolved in a very short time while the task does not have to be based on exact specifications and therefore several solutions might exist. A Java application was created within the scope of the thesis; its core comprises a genetic algorithm and it was used for the purpose of modelling of specific teams. The results provided by the application were subsequently verified by the means of creation of teams used for completion of new tasks and monitoring their activities in practice. Furthermore, the model verification of teams previously created solely on the basis of experience of executives was performed and the respective results were compared.
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Proposal of prediction model sales of selected food commodities
Řešetková, Dagmar ; Dostál, Petr (referee) ; Krčmarský, Miroslav (referee) ; Zelinka, Ivan (referee) ; Rais, Karel (advisor)
The dissertation is generally focused on the use of artificial intelligence tools in practice and with regard to the focus of study in the field of Management and Business Economics at using the tools of artificial intelligence in corporate practice, as a tool for decision support at the operational and tactical level management. In the narrower sense, the task deals with the proposal of the prediction sales model of selected food commodities. The proposed model is designed to serve as a substitute for a human expert in support decision-making process in the purchase of selected commodities, especially when training new staff and extend the currently used methods of managerial decision-making about artificial intelligence tools for company management and existing employees. The aim of this dissertation is the design prediction sales model of selected food commodities (apples and potatoes) for specific wholesale of fruit and vegetable operating in the Czech Republic. To become familiar with the behaviour of selected commodities were used primary and secondary research as well and knowledge gained from Czech and foreign literature sources and research. The resulting predictive model is developed using statistical analysis of time series and the sales prediction proceeds using the tools of artificial intelligence and is modeled by an artificial neural network. The dissertation in the practical part also contains proposals for the use of the prediction model and partial processing procedures for: • practice, • theory, • pedagogical activities.
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