A hybrid multiobjective optimization algorithm applied to space trajectory optimization

This paper presents an algorithm for multiobjective optimization that blends together a number of heuristics. A population of agents combines heuristics that aim at exploring the search space both globally and in a neighborhood of each agent. These heuristics are complemented with two restart mechanisms and a combination of a local and global archive. The hybrid algorithm is tested at first on a set of standard problems and then on three specific problems in space trajectory design. The performance of the proposed hybrid algorithm is compared against NSGA-II.