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Acceleration of Particle Swarm Optimization Using GPUs
Krézek, Vladimír ; Schwarz, Josef (referee) ; Jaroš, Jiří (advisor)
This work deals with the PSO technique (Particle Swarm Optimization), which is capable to solve complex problems. This technique can be used for solving complex combinatorial problems (the traveling salesman problem, the tasks of knapsack), design of integrated circuits and antennas, in fields such as biomedicine, robotics, artificial intelligence or finance. Although the PSO algorithm is very efficient, the time required to seek out appropriate solutions for real problems often makes the task intractable. The goal of this work is to accelerate the execution time of this algorithm by the usage of Graphics processors (GPU), which offers higher computing potential while preserving the favorable price and size. The boolean satisfiability problem (SAT) was chosen to verify and benchmark the implementation. As the SAT problem belongs to the class of the NP-complete problems, any reduction of the solution time may broaden the class of tractable problems and bring us new interesting knowledge.
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DNA Computing and Applications
Fiala, Jan ; Petrlík, Jiří (referee) ; Bidlo, Michal (advisor)
This thesis focuses on the design and implementation of an application involving the principles of DNA computing simulation for solving some selected problems. DNA computing represents an unconventional computing paradigm that is totally different from the concept of electronic computers. The main idea of DNA computing is to interpret the DNA as a medium for performing computation. Despite the fact, that DNA reactions are slower than operations performed on computers, they may provide some promising features in the future. The DNA operations are based on two important aspects: massive parallelism and principle of complementarity. There are many important problems for which there is no algorithm that would be able to solve the problem in a polynomial time using conventional computers. Therefore, the solutions of such problems are searched by exploring the entire state space. In this case the massive parallelism of the DNA operations becomes very important in order to reduce the complexity of finding a solution.
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Evolutionary Algorithms in the Task of Boolean Satisfiability
Serédi, Silvester ; Vašíček, Zdeněk (referee) ; Sekanina, Lukáš (advisor)
The goal of this Master's Thesis is finding a SAT solving heuristic by the application of an evolutionary algorithm. This thesis surveys various approaches used in SAT solving and some variants of evolutionary algorithms that are relevant to this topic. Afterwards the implementation of a linear genetic programming system that searches for a suitable heuristic for SAT problem instances is described, together with the implementation of a custom SAT solver which expoloits the output of the genetic program. Finally, the achieved results are summarized.
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DNA Computing and Applications
Fiala, Jan ; Petrlík, Jiří (referee) ; Bidlo, Michal (advisor)
This thesis focuses on the design and implementation of an application involving the principles of DNA computing simulation for solving some selected problems. DNA computing represents an unconventional computing paradigm that is totally different from the concept of electronic computers. The main idea of DNA computing is to interpret the DNA as a medium for performing computation. Despite the fact, that DNA reactions are slower than operations performed on computers, they may provide some promising features in the future. The DNA operations are based on two important aspects: massive parallelism and principle of complementarity. There are many important problems for which there is no algorithm that would be able to solve the problem in a polynomial time using conventional computers. Therefore, the solutions of such problems are searched by exploring the entire state space. In this case the massive parallelism of the DNA operations becomes very important in order to reduce the complexity of finding a solution.
|
|
|
Evolutionary Algorithms in the Task of Boolean Satisfiability
Serédi, Silvester ; Vašíček, Zdeněk (referee) ; Sekanina, Lukáš (advisor)
The goal of this Master's Thesis is finding a SAT solving heuristic by the application of an evolutionary algorithm. This thesis surveys various approaches used in SAT solving and some variants of evolutionary algorithms that are relevant to this topic. Afterwards the implementation of a linear genetic programming system that searches for a suitable heuristic for SAT problem instances is described, together with the implementation of a custom SAT solver which expoloits the output of the genetic program. Finally, the achieved results are summarized.
|
|
|
Acceleration of Particle Swarm Optimization Using GPUs
Krézek, Vladimír ; Schwarz, Josef (referee) ; Jaroš, Jiří (advisor)
This work deals with the PSO technique (Particle Swarm Optimization), which is capable to solve complex problems. This technique can be used for solving complex combinatorial problems (the traveling salesman problem, the tasks of knapsack), design of integrated circuits and antennas, in fields such as biomedicine, robotics, artificial intelligence or finance. Although the PSO algorithm is very efficient, the time required to seek out appropriate solutions for real problems often makes the task intractable. The goal of this work is to accelerate the execution time of this algorithm by the usage of Graphics processors (GPU), which offers higher computing potential while preserving the favorable price and size. The boolean satisfiability problem (SAT) was chosen to verify and benchmark the implementation. As the SAT problem belongs to the class of the NP-complete problems, any reduction of the solution time may broaden the class of tractable problems and bring us new interesting knowledge.
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