No description available.

The purpose of this study is to discuss the fitness of using PHMGA (Parallel Multilevel Hybrid Genetic Algorithm), which is a fast and efficient method, in the geometrically nonlinear structural optimization. Parallel genetic algorithms can solve the problem of traditional sequential genetic algorithms, such as premature convergence, large number of function evaluations, and a difficulty in setting parameters. By using several concurrent sub-population, parallel genetic algorithms can avoid premature convergence resulting from the single genetic searching environment of sequential genetic algorithms. It is useful to speed up the operation rate of joining timely multilevel optimization with parallel genetic algorithms. Because multilevel optimization can resolve one problem into several smaller subproblems, each subproblem is independent and not interference with one another. Then the subsystem of each level can be connected by sensitivity analysis. So we can solve the entire problem. Because each subproblem contains less variables and constrains, it can achieve the faster converge rate of the entire optimization. PHMGA integrates advantages of two methods including the parallel genetic algorithms and the multilevel optimization. In this study, PHMGA is adopted to solve several design optimization problems for nonlinear geometrically trusses on the parallel computer IBM SP2. The use of PHMGA helps reduce execution time because of integrating a multilevel optimization and a parallel technique. PHMGA helps speed up the searching efficiency in solving structural optimization problems of nonlinear truss. It is hoped that this study will demonstrate PHMGA is an efficient and powerful tool in solving large geometrically nonlinear structural optimization problems.

Essa dissertação apresenta a implementação de um algoritmo genético paralelo utilizando o modelo de granularidade grossa, também conhecido como modelo das ilhas, para sistemas embutidos multiprocessados. Os sistemas embutidos multiprocessados estão tornando-se cada vez mais complexos, pressionados pela demanda por maior poder computacional requerido pelas aplicações, principalmente de multimídia, Internet e comunicações sem fio, que são executadas nesses sistemas. Algumas das referidas aplicações estão começando a utilizar algoritmos genéticos, que podem ser beneficiados pelas vantagens proporcionadas pelo processamento paralelo disponível em sistemas embutidos multiprocessados. No algoritmo genético paralelo do modelo das ilhas, cada processador do sistema embutido é responsável pela evolução de uma população de forma independente dos demais. A fim de acelerar o processo evolutivo, o operador de migração é executado em intervalos definidos para realizar a migração dos melhores indivíduos entre as ilhas. Diferentes topologias lógicas, tais como anel, vizinhança e broadcast, são analisadas na fase de migração de indivíduos. Resultados experimentais são gerados para a otimização de três funções encontradas na literatura. / This dissertation presents an implementation of a parallel genetic algorithm using the coarse grained model, also known as the islands model, targeted to MPSoCs systems. MPSoC systems are becoming more and more complex, due to the greater computational power demanded by applications, mainly those that deal with multimedia, Internet and wireless communications, which are executed within these systems. Some of these applications are starting to use genetic algorithms, that can benefit from the parallel processing offered by MPSoC. In the island model for parallel genetic algorithm, each processor is responsible for evolving the corresponding population independently from the others. Aiming at accelerating the evolutionary process, the migration operator is executed periodically in order to migrate the best individuals among islands. Different logic topologies, such as ring, neighborhood and broadcast, are analyzed during the migration step. Experimental results are generated for the optimization of three functions found in the literature.

Essa dissertação apresenta a implementação de um algoritmo genético paralelo utilizando o modelo de granularidade grossa, também conhecido como modelo das ilhas, para sistemas embutidos multiprocessados. Os sistemas embutidos multiprocessados estão tornando-se cada vez mais complexos, pressionados pela demanda por maior poder computacional requerido pelas aplicações, principalmente de multimídia, Internet e comunicações sem fio, que são executadas nesses sistemas. Algumas das referidas aplicações estão começando a utilizar algoritmos genéticos, que podem ser beneficiados pelas vantagens proporcionadas pelo processamento paralelo disponível em sistemas embutidos multiprocessados. No algoritmo genético paralelo do modelo das ilhas, cada processador do sistema embutido é responsável pela evolução de uma população de forma independente dos demais. A fim de acelerar o processo evolutivo, o operador de migração é executado em intervalos definidos para realizar a migração dos melhores indivíduos entre as ilhas. Diferentes topologias lógicas, tais como anel, vizinhança e broadcast, são analisadas na fase de migração de indivíduos. Resultados experimentais são gerados para a otimização de três funções encontradas na literatura. / This dissertation presents an implementation of a parallel genetic algorithm using the coarse grained model, also known as the islands model, targeted to MPSoCs systems. MPSoC systems are becoming more and more complex, due to the greater computational power demanded by applications, mainly those that deal with multimedia, Internet and wireless communications, which are executed within these systems. Some of these applications are starting to use genetic algorithms, that can benefit from the parallel processing offered by MPSoC. In the island model for parallel genetic algorithm, each processor is responsible for evolving the corresponding population independently from the others. Aiming at accelerating the evolutionary process, the migration operator is executed periodically in order to migrate the best individuals among islands. Different logic topologies, such as ring, neighborhood and broadcast, are analyzed during the migration step. Experimental results are generated for the optimization of three functions found in the literature.

This thesis deals with analysis of genetic algorithms. It is focused on various approaches to creation of new populations. A comparison between basic principles of operation of genetic algorithms and processes occurring in living organisms is drawn here. Some methods of application of particular steps of genetic algorithms are introduced and a suitability of the methods to certain types of problems is considered. The main goal in the thesis is to apply genetic algorithms in solving three types of optimization problems, namely the solution of functions with a single major extreme, functions with flat (slight) extreme and also functions with many local extremes.