Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO
This addresses trajectory optimization for space missions, but it appears incremental as it focuses on algorithm comparisons and hybridization.
The paper tackles the problem of designing spacecraft trajectories for missions visiting multiple celestial bodies by framing it as a multi-objective bilevel optimization problem and comparing Beam Search algorithms, with a new hybridization called Beam P-ACO showing lower parameter sensitivity and superior worst-case performance.
The design of spacecraft trajectories for missions visiting multiple celestial bodies is here framed as a multi-objective bilevel optimization problem. A comparative study is performed to assess the performance of different Beam Search algorithms at tackling the combinatorial problem of finding the ideal sequence of bodies. Special focus is placed on the development of a new hybridization between Beam Search and the Population-based Ant Colony Optimization algorithm. An experimental evaluation shows all algorithms achieving exceptional performance on a hard benchmark problem. It is found that a properly tuned deterministic Beam Search always outperforms the remaining variants. Beam P-ACO, however, demonstrates lower parameter sensitivity, while offering superior worst-case performance. Being an anytime algorithm, it is then found to be the preferable choice for certain practical applications.