Structural optimization using evolution strategies and neural networks

The objective of this paper is to investigate the efficiency of combinatorial optimization methods, in particular algorithms based on evolution strategies (ES) when incorporated into the solution of large-scale, continuous or discrete, structural optimization problems. Two types of applications have been investigated, namely shape and sizing structural optimization problems. Furthermore, a neural network (NN) model is used in order to replace the structural analysis phase and to compute the necessary data for the ES optimization procedure. The use of NN was motivated by the time-consuming repeated analyses required by ES during the optimization process. A back propagation algorithm is implemented for training the NN using data derived from selected analyses. The trained NN is then used to predict, within an acceptable accuracy, the values of the objective and constraint functions. The numerical tests presented demonstrate the computational advantages of the proposed approach which become more pronounced in large-scale optimization problems. (C) 1998 Elsevier Science S.A.