Detalhes bibliográficos
| Ano de defesa: |
2019 |
| Autor(a) principal: |
Eric Demetrius de Castro Barroca |
| Orientador(a): |
Fabiano Luis de Sousa,
Ronan Arraes Jardim Chagas |
| Banca de defesa: |
Roberto Luiz Galski,
Antonio Augusto Chaves |
| Tipo de documento: |
Dissertação
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
eng |
| Instituição de defesa: |
Instituto Nacional de Pesquisas Espaciais (INPE)
|
| Programa de Pós-Graduação: |
Programa de Pós-Graduação do INPE em Engenharia e Gerenciamento de Sistemas Espaciais
|
| Departamento: |
Não Informado pela instituição
|
| País: |
BR
|
| Link de acesso: |
http://urlib.net/sid.inpe.br/mtc-m21c/2019/05.15.23.22
|
Resumo: |
In this work a new adaptive evolutionary algorithm derived from a stochastic algorithm for design optimization called Generalized Extremal Optimization (GEO) is introduced. It eliminates the single free parameter of GEO by controlling its value during the search by an adaptive approach which improved GEO performance significantly, even when considering the best GEO configurations. Nonetheless, it maintains the algorithm principal characteristics of dealing with continuous, discrete and integer design variables on convex or disjoint spaces while respecting design constrains. This new algorithm, called Adaptive Generalized Extremal Optimization (A-GEO), is implemented in two variations and applied to a multidisciplinary optimization problem of spacecraft engineering, showing the potential of the new methods in solving real engineering problems. |
