Combining response surfaces and evolutionary strategies for multiobjective pareto-optimization in electromagnetics

Abstract

The reduction of computational cost of evolutionary multiobjective Pareto-optimization algorithms is necessary when time-consuming objective functions evaluation is required. To this end neural network interpolation techniques are used for objective response surface building; evolutionary multiobjective Pareto-optimization is then performed on interpolated functions. Both analytical multiobjective test problems and numerical electromagnetic design problems are considered; the study of Pareto optimal front interpolation accuracy versus neural network training cost is performed.