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Applications of PSO-Based Algorithms
Hudecová, Patrícia ; Husa, Jakub (oponent) ; Bidlo, Michal (vedoucí práce)
This work aimed to study already existing optimization algorithms from the field of algorithms inspired by collective behavior and apply them to problems usable in practice in practice. Subsequent efforts to improve existing solutions with possible modifications and evaluation of the results. The algorithms chosen were particle swarm optimization, gravitational search algorithm, and black hole algorithm. The optimization problems were the traveling salesman problem and knapsack, which are applicable in various practical fields.
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Overview of Actual Approaches to Optimization
Hudecová, Patrícia ; Šůstek, Martin (oponent) ; Zbořil, František (vedoucí práce)
This work aimed to study some of the optimization algorithms inspired by nature and to test their success in finding the extreme of a function on various functions. Four algorithms were selected, namely the bat algorithm, the firefly algorithm, the flower pollination algorithm, and the black hole algorithm. The Griewank function, the Rastringin function, and the Rosenbrock function were chosen as test functions for finding the extreme of the function. The work contains a description of individual algorithms, a description of test functions and a description of the experiments, and an evaluation of the success of the algorithms.
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Overview of Actual Approaches to Optimization
Hudecová, Patrícia ; Šůstek, Martin (oponent) ; Zbořil, František (vedoucí práce)
This work aimed to study some of the optimization algorithms inspired by nature and to test their success in finding the extreme of a function on various functions. Four algorithms were selected, namely the bat algorithm, the firefly algorithm, the flower pollination algorithm, and the black hole algorithm. The Griewank function, the Rastringin function, and the Rosenbrock function were chosen as test functions for finding the extreme of the function. The work contains a description of individual algorithms, a description of test functions and a description of the experiments, and an evaluation of the success of the algorithms.
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