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The design of spacecraft trajectories for missions visiting multiplecelestial bodies is here framed as a multi-objective bilevel optimizationproblem. A comparative study is performed to assess the performance ofdifferent Beam Search algorithms at tackling the combinatorial problem offinding the ideal sequence of bodies. Special focus is placed on thedevelopment of a new hybridization between Beam Search and the Population-basedAnt Colony Optimization algorithm. An experimental evaluation shows allalgorithms achieving exceptional performance on a hard benchmark problem. It isfound that a properly tuned deterministic Beam Search always outperforms theremaining variants. Beam P-ACO, however, demonstrates lower parametersensitivity, while offering superior worst-case performance. Being an anytimealgorithm, it is then found to be the preferable choice for certain practicalapplications.

Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO