A Neural Network Based Generalized Response Surface Multiobjective Evolutionary Algorithm
نویسنده
چکیده
The practical use of multiobjective optimization tools in industry is still an open issue. A strategy for reduction of objective function calls is often essential, at a fixed degree of Pareto Optimal Front (POF) approximation accuracy . To this aim an extension of single-objective NN-based GRS methods to Pareto Optimal Front (POF) approximation is proposed. Such an extension is not at all straightforward due to the complex relation between the POF and Pareto Optimal Set (POS). As a consequence of such a complexity, it is extremely difficult to identify a multi-objective analogue of the single-objective current optimum region; consequently the design domain search space zooming strategy, which is the core of a GRS method, is to be carefully reconsidered when POF approximation is concerned. Keywords— Evolutionary multiobjective optimization, NN interpolation, response surface methods.
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