Abstract

Engineering design often requires the optimisation of multiple objectives, and
becomes significantly more difficult and time consuming when the response
surfaces are multimodal, rather than unimodal. A surrogate model, also known
as a metamodel, can be used to replace expensive computer simulations,
accelerating single and multi-objective optimisation and the exploration of new
design concepts. The main research focus of this work is to investigate the use
of a neural network surrogate model to improve optimisation of multimodal
surfaces.

Several significant contributions derive from evaluating the Cascade Correlation
neural network as the basis of a surrogate model. The contributions to the
neural network community ultimately outnumber those to the optimisation
community.

The effects of training this surrogate on multimodal test functions are explored.
The Cascade Correlation neural network is shown to map poorly such response
surfaces. A hypothesis for this weakness is formulated and tested. A new
subdivision technique is created that addresses this problem; however, this new
technique requires excessively large datasets upon which to train.

The primary conclusion of this work is that Cascade Correlation neural networks
form an unreliable basis for a surrogate model, despite successes reported in
the literature.

A further contribution of this work is the enhancement of an open source
optimisation toolkit, achieved by the first integration of a truly multi-objective
optimisation algorithm.