Abstract
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.
Code available at https://github.com/lfsimoes/beam_paco__gtoc5.
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Notes
- 1.
The NASA/JPL team’s GTOC7 submission report and workshop slides, containing details of their ACO deployment, can be found in the GTOC portal [1].
- 2.
- 3.
Legs failing this check are immediately discarded, saving computation time that would otherwise be spent generating Lambert arcs with excessive \(\varDelta V\).
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Acknowledgment
Luís F. Simões was supported by FCT (Ministério da Ciência e Tecnologia) Fellowship SFRH/BD/84381/2012.
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Simões, L.F., Izzo, D., Haasdijk, E., Eiben, A.E. (2017). Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO. In: Hu, B., López-Ibáñez, M. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2017. Lecture Notes in Computer Science(), vol 10197. Springer, Cham. https://doi.org/10.1007/978-3-319-55453-2_10
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