Computing the Set of Epsilon-Efficient Solutions in Multiobjective Space Mission Design

In this work, we consider multi-objective space mission design problems.We will start from the need, from a practical point of view, to consider in addition to the (Pareto) optimal solutions also nearly optimal ones.In fact, extending the set of solutions, for a given mission, to those nearly optimal significantly increases the number of options for the decision maker and gives a measure of the size of the launch windows corresponding to each optimal solution, i.e. a measure of its robustness.Whereas the possible loss of such approximate solutions compared to optimal-and possibly even 'better'-ones is dispensable.For this, we will examine several typical problems in space trajectory design-a bi-impulsive transfer from the Earth to the asteroid Apophis and two low-thrust multi-gravity assist transfers-and demonstrate the possible benefit of the novel approach.Further, we will present a multi-objective evolutionary algorithm which is designed for this purpose.