INTEGRATED DISCRETE AND CONFIGURATION OPTIMIZATION OF TRUSSES USING GENETIC ALGORITHMS

The application of genetic algorithms to integrated discrete and configuration optimization of trusses is presented. It is mathematically formulated as a constrained nonlinear optimization problem with a mix of discrete sizing and continuous configuration variables. The components of genetic algorithms are described. The discrete sizing variables are treated by constructing mapping relationships between binary digit strings and discrete values by the medium of unsigned decimal integers. Careful attention has been paid to modify the genetic algorithms to promote computational efficiency. The solution procedures are stated. Several examples are solved in two ways to verify that considerable weight saving can be achieved by adding in configuration design variables, and the results also show the efficiency and robustness of genetic algorithms.