|
|
Evolutionary Design of Quantum Operator
Kraus, Pavel ; Mrázek, Vojtěch (referee) ; Bidlo, Michal (advisor)
The goal of this thesis is to utilize various evolutionary algorithms for quantum operator design in the form of unitary matrices in direct representation. Evolution strategy, differential evolution, Particle Swarm Optimization and artificial bee colony algorithms were chosen. In this thesis, the third and fourth algorithms were used for the first time in relation to quantum operator design. The experiments have shown that the utilization of direct representation gives results of acceptable quality.
|
|
|
Unmanned aerial vehicle path planning by evolutionary algorithms
Miloševič, Filip ; Šoustek, Petr (referee) ; Kůdela, Jakub (advisor)
This bachelor thesis deals with the problem of optimizing UAV routes in 3D space. It consists of a review of the current knowledge regarding the issue and the implementation of a program with a graphical interface that can create an optimized route using the Artificial Bee Colony, Particle Swarm Optimization and Whale Optimization Algorithms. Subsequently, a series of simulations were performed using the program, in which the Artificial Bee Colony algorithm proved to be the most effective algorithm in solving this issue.
|
|
|
Evolutionary Design of Quantum Operator
Kraus, Pavel ; Mrázek, Vojtěch (referee) ; Bidlo, Michal (advisor)
The goal of this thesis is to utilize various evolutionary algorithms for quantum operator design in the form of unitary matrices in direct representation. Evolution strategy, differential evolution, Particle Swarm Optimization and artificial bee colony algorithms were chosen. In this thesis, the third and fourth algorithms were used for the first time in relation to quantum operator design. The experiments have shown that the utilization of direct representation gives results of acceptable quality.
|
|
|
Artificial Bee Colony
Jukl, Jan ; Pangrác, Ondřej (advisor) ; Hušek, Radek (referee)
The minimum vertex cover (MVC) problem is a well-known NP-hard prob- lem. This thesis presents the Artificial Bee Colony (ABC) algorithm and two genetic algorithm approaches to solve this problem. The ABC algorithm is an optimization algorithm based on the intelligent behaviour of a honey bee swarm. It was first proposed for unconstrained optimization problems and showed that it is superior in performance on these kinds of problems. In this thesis, the ABC algorithm has been extended to solving the minimum vertex cover problem and applied to DIMACS and BHOSLIB benchmarks. The results produced by the ABC, the binary decision diagram based genetic algorithm and the MVC-aware genetic algorithm have been compared.
|
guest ::
