Structural optimization using evolution strategies and neural networksStructural optimization using evolution strategies and neural networks Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2015-10-081998enThe 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. http://creativecommons.org/licenses/by/4.0/Computer Methods in Applied Mechanics and Engineering 1-4156309-333 Papadrakakis, Manolis Lagaros, Nikos D., 1970- Yiannis Tsompanakis --Cases, clinical reports, statistics --Statistical data statistics cases clinical reports statistics statistical data