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This paper shows how a discrete approximation of a Pareto front can be refined with polynomial interpolation. For this we exploit the information given by the discrete samples of the Pareto front and in addition we use parametric sensitivity information from these samples. The pararmetric sensitivities are afterwards used to ensure feasibility of the obtained interpolated solutions by applying an iterative self correction algorithm. Results are shown for an bi-objective example.

This paper shows how an electrical distribution network can be modeled as constraints of a continuous nonlinear optimization problem. The objective is to minimize the deviation from a given nominal voltage. We explain how this setup can be used to optimize wire diameters within the network. Thus we find the optimal expansion of the network by identifying wires which should be enforced. The results are shown by applying the functionality to a real world example.