Evolutionary Optimisation of Mechanical Structures: Towards an Integrated Optimisation

Abstract

This work results from the synthesis of author’s works on the applications of stochastic techniques (genetic algorithms with neural networks) for the optimisation of mechanical structures. The emphasis of this work is on the practical aspects and the feasibility of the aformentioned techniques. The research strategy consists in substituting, for finite element calculations in the optimisation process, an approximate response of a neural network. More precisely, the paper describes the use of backpropagation neural networks in creating function approximations for use in computationally intensive design optimisation based on genetic algorithms. An example of application for space frame optimisation of a helicopter tail boom is given in this paper, for which we can talk of integrated optimisation. This example (including displacement and frequency constraints) show the use of neural networks as a function approximation strategy to limit the computational costs associated with stochastic search methods.