{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/multi-rendezvous-spacecraft-trajectory","title":"Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO","arxiv_id":"1704.00702","date":"2017-04-03","proceeding":null,"authors":["Luís F. Simões","Dario Izzo","Evert Haasdijk","A. E. Eiben"],"abstract":"The design of spacecraft trajectories for missions visiting multiple\ncelestial bodies is here framed as a multi-objective bilevel optimization\nproblem. A comparative study is performed to assess the performance of\ndifferent Beam Search algorithms at tackling the combinatorial problem of\nfinding the ideal sequence of bodies. Special focus is placed on the\ndevelopment of a new hybridization between Beam Search and the Population-based\nAnt Colony Optimization algorithm. An experimental evaluation shows all\nalgorithms achieving exceptional performance on a hard benchmark problem. It is\nfound that a properly tuned deterministic Beam Search always outperforms the\nremaining variants. Beam P-ACO, however, demonstrates lower parameter\nsensitivity, while offering superior worst-case performance. Being an anytime\nalgorithm, it is then found to be the preferable choice for certain practical\napplications.","url_abs":"http://arxiv.org/abs/1704.00702v1","url_pdf":"http://arxiv.org/pdf/1704.00702v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"multi-rendezvous-spacecraft-trajectory","repo_url":"https://github.com/lfsimoes/beam_paco__gtoc5","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"bilevel-optimization","task_name":"Bilevel Optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}