GLOBAL, MULTI-OBJECTIVE TRAJECTORY OPTIMIZATION WITH PARAMETRIC SPREADING

Mission design problems are often characterized by multiple, competing trajectory optimization objectives. Recent multi-objective trajectory optimization formulations enable generation of globally-optimal, Pareto solutions via a multi objective genetic algorithm. A byproduct of these formulations is that clustering in design space can occur in evolving the population towards the Pareto front. This clustering can be a drawback, however, if parametric evaluations of design variables are desired. This effort addresses clustering by incorporating operators that encourage a uniform spread over specified design variables while maintaining Pareto front representation. The algorithm is demonstrated on low- and high-thrust mission examples, and enhanced multidimensional visualization strategies are presented.