Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
Design and Analysis of Simulation Experiments
Design and Analysis of Simulation Experiments
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
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In this paper we propose an integrated pre-design step framework using multiobjective evolutionary optimization and a decision support tool. The tailored genetic algorithm relies on specific fitness function which enables to deal with a high number of objectives. Moreover, surrogate models have been integrated so as to speed up objective functions evaluations which are usually expensive in case of mechanical product pre-design step. An automatic post-treatment of Pareto optimal solutions is proposed in order to synthesize a large multidimensional database into a restricted number of typings. This latter step is of particular importance since it affords designer a powerful decision support tool.