Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
9673768 | Advances in Engineering Software | 2005 | 11 Pages |
Abstract
Optimum design of large-scale structures by standard genetic algorithm (GA) makes the computational burden of the process very high. To reduce the computational cost of standard GA, two different strategies are used. The first strategy is by modifying the standard GA, called virtual sub-population method (VSP). The second strategy is by using artificial neural networks for approximating the structural analysis. In this study, radial basis function (RBF), counter propagation (CP) and generalized regression (GR) neural networks are used. Using neural networks within the framework of VSP creates a robust tool for optimum design of structures.
Related Topics
Physical Sciences and Engineering
Computer Science
Software
Authors
Eysa Salajegheh, Saeed Gholizadeh,