zur Erlangung des Grades eines

The purpose of multiobjective optimization is to find solutions that are optimal regarding several goals. In the branch of vector or Pareto optimization all these goals are considered to be of equal importance, so that compromise solutions that cannot be improved regarding one goal without deteriorating in another are Pareto-optimal. A variety of quality measures exist to evaluate approximations of the Pareto-optimal set generated by optimizers, wherein the hypervolume is the most sig-nificant one, making the hypervolume calculation a core problem of multiobjective optimization. This thesis tackles that challenge by providing a new hypervolume al-gorithm from computational geometry and analyzing the problem’s computational complexity. Evolutionary multiobjective optimization algorithms (EMOA) are state-of-the-art methods for Pareto optimization, wherein the hypervolume-based algorithms be-long to the most powerful ones, among them the popular SMS-EMOA. After it