Article ID: | iaor20081975 |
Country: | United Kingdom |
Volume: | 39 |
Issue: | 4 |
Start Page Number: | 381 |
End Page Number: | 396 |
Publication Date: | Jun 2007 |
Journal: | Engineering Optimization |
Authors: | Hajela P., Canyurt O.E. |
Keywords: | computational analysis: parallel computers |
Genetic algorithms (GAs) have received considerable recent attention in problems of design optimization. The mechanics of population-based search in GAs are highly amenable to implementation on parallel computers. The present article describes a fine-grained model of parallel GA implementation that derives from a cellular-automata-like computation. The central idea behind the cellular genetic algorithm (CGA) approach is to treat the GA population as being distributed over a 2-D grid of cells, with each member of the population occupying a particular cell and defining the state of that cell. Evolution of the cell state is tantamount to updating the design information contained in a cell site and, as in cellular automata computations, takes place on the basis of local interaction with neighbouring cells. A special focus of the article is in the use of cellular automata (CA)-based models for structural analysis in conjunction with the CGA approach to optimization. In such an approach, the analysis and optimization are evolved simultaneously in a unified cellular computational framework. The article describes the implementation of this approach and examines its efficiency in the context of representative structural optimization problems.