A neural network based generalized response surface multiobjective evolutionary algorithm

The practical use of multiobjective optimization tools in industry is still an open issue. A strategy for the reduction of objective functions is often essential, at a fixed degree of Pareto optimal front (POF) approximation accuracy. An extension of single-objective NN-based generalized response surfaces (GRS) methods to POF approximation is proposed. Such an extension is not at all straightforward due to the complex relation between the POF and Pareto optimal set. As a consequence of such complexity, it is extremely difficult to identify a multiobjective analogue of the single-objective current optimum region. Consequently, the design domain search space zooming strategy, which is the core of the GRS method, is to be carefully reconsidered when POF approximation is concerned.