Over the last few years, research activity in approximation (e.g. metamodels) and optimization (e.g. genetic algorithms) methods has improved upon current practices in engineering design and optimization of complex systems with respect to multiple performance metrics, by reducing the number of evaluations of the system’s model that are needed to obtain the set of non-dominated solutions to a given multi-objetive optimal design problem. To this end, several authors have proposed to enhance Multi-Objective Genetic Algorithms (MOGAs) with metamodel-based pre-screening criteria (PSC), so that only those solutions that have the most potential to improve the current approximation of the Pareto Front are evaluated with the (costly) system model. The main goals of this work are to compare the performance of several PSC with an array of test functions taken from the literature, and to study the potential effect on their effectiveness and efficiency of using multi-response metamodels, instead of building independent, individual metamodels for each objective function, as has been done in previous work. Our preliminary results show that no single PSC is observed to be superior overall, though the Minimum of Minimum Distances and Expected Improvement criteria outperformed other PSC in most cases. Results also show that the use of multi-response metamodels improved both the effectiveness and efficiency of PSC and the quality of solution at the end of the optimization in 50% to 60% of test cases.

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