Efficiency of tree-search like heuristics to solve complex mixed-integer programming problems applied to space trajectory design

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dc.contributor.authorBellome, A.
dc.contributor.authorCarrillo, M.
dc.contributor.authorSánchez, J. P.
dc.contributor.authorSer, J. Del
dc.contributor.authorKemble, S.
dc.contributor.authorFelicetti, L.
dc.contributor.institutionIA
dc.date.accessioned2024-07-24T11:52:05Z
dc.date.available2024-07-24T11:52:05Z
dc.date.issued2021
dc.descriptionPublisher Copyright: © 2021 by Authors.
dc.description.abstractIn the past, space trajectory optimization was limited to optimal design of transfers to single destinations, where optimality refers to minimum propellant consumption or transfer time. New technologies, and a more daring approach to space, are today making the space community consider missions that target multiple destinations. In the present paper, we focus on missions that aim to visit multiple asteroids within a single launch. The trajectory design of these missions is complicated by the fact that the asteroid sequences are not known a priori but are the objective of the optimization itself. Usually, these problems are formulated as global optimization (GO) problems, under the formulation of mixed-integer non-linear programming (MINLP), on which the decision variables assume both continuous and discrete values. However, beyond the aim of finding the global optimum, mission designers are usually interested in providing a wide range of mission design options reflecting the multi-modality of the problems at hand. In this sense, a Constraint Satisfaction Problem (CSP) formulation is also relevant. With the present paper we focus on these two needs (i.e. tackling both the GO and the CSP) for the asteroid tour problem. First, a tree-search algorithm based upon the Bellman's principle of optimality is described using dynamic programming approach to address the feasibility of solving the GO problem. This results in an efficient and scalable procedure to obtain global optimum solutions within large datasets of asteroids. Secondly, tree-search strategies like Beam Search and Ant Colony Optimization with back-tracking are tested over the CSP formulations. Results show that BS handles better the multi-modality of the search space compared to ACO, as this bias the elite solutions which resulting in the diversity loss.en
dc.description.statusPeer reviewed
dc.identifier.citationBellome , A , Carrillo , M , Sánchez , J P , Ser , J D , Kemble , S & Felicetti , L 2021 , Efficiency of tree-search like heuristics to solve complex mixed-integer programming problems applied to space trajectory design . in IAF Astrodynamics Symposium 2021 - Held at the 72nd International Astronautical Congress, IAC 2021 . Proceedings of the International Astronautical Congress, IAC , vol. C1 , International Astronautical Federation, IAF , IAF Astrodynamics Symposium 2021 at the 72nd International Astronautical Congress, IAC 2021 , Dubai , United Arab Emirates , 25/10/21 .
dc.identifier.citationconference
dc.identifier.isbn9781713843078
dc.identifier.issn0074-1795
dc.identifier.urihttps://hdl.handle.net/11556/2152
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85127825942&partnerID=8YFLogxK
dc.language.isoeng
dc.publisherInternational Astronautical Federation, IAF
dc.relation.ispartofIAF Astrodynamics Symposium 2021 - Held at the 72nd International Astronautical Congress, IAC 2021
dc.relation.ispartofseriesProceedings of the International Astronautical Congress, IAC
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsAsteroids
dc.subject.keywordsConstraint satisfaction problem
dc.subject.keywordsDynamic programming
dc.subject.keywordsGlobal optimization
dc.subject.keywordsAerospace Engineering
dc.subject.keywordsAstronomy and Astrophysics
dc.subject.keywordsSpace and Planetary Science
dc.titleEfficiency of tree-search like heuristics to solve complex mixed-integer programming problems applied to space trajectory designen
dc.typeconference output
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