Topological Optimum Design using Genetic Algorithms

Structural topology optimization is addressed through Genetic Algorithms: A set of designs is evolved following the Darwinian survival-of-ttest principle. The goal is to optimize the weight of the structure under displacement constraints. This approach demonstrates high exibility, and breaks many limits of standard optimization algorithms, in spite of the heavy requirements in term of computational eeort: Alternate optimal solutions to the same problem can be found; Structures can be optimized with respect to multiple loadings; The prescribed loadings can be applied on the unknown boundary of the solution, rather than on the xed boundary of the design domain; Diierent materials as well as diierent mechanical models can be used, as witnessed by the rst results of Topological Optimum Design ever obtained in the large displacements model. But these results could not have been obtained without careful speciic handling of the speciic aspects of topological genetic optimization: First, speciic genetic operators (crossover, mutation) were introduced; Second, special attention was paid to the design of the objective function; The nonlinear geometrical eeects of the large displacement model lead to non viable solutions, unless some constraints are imposed on the stress eld.