Robust Estimation of Distribution Algorithms via Fitness Landscape Analysis for Optimal Low-Thrust Orbital Maneuvers

One particular kind of evolutionary algorithms known as Estimation of Distribution Algorithms (EDAs) has gained the attention of the aerospace industry for its ability to solve nonlinear and complicated problems, particularly in the optimization of space trajectories during on-orbit operations of sa...

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Autor: Shirazi, A.
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Recursos:Basque Center for Applied Mathematics (BCAM)
Repositorio:BIRD. BCAM's Institutional Repository Data
OAI Identifier:oai:bird.bcamath.org:20.500.11824/1719
Acesso em linha:http://hdl.handle.net/20.500.11824/1719
Access Level:acceso abierto
Palavra-chave:Aerospace Engineering
Evolutionary Algorithm
Optimization
Spacecraft
Estimation of Distribution Algorithms
Astrodynamics
Orbital Mechanics
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spelling Robust Estimation of Distribution Algorithms via Fitness Landscape Analysis for Optimal Low-Thrust Orbital ManeuversShirazi, A.Aerospace EngineeringEvolutionary AlgorithmOptimizationSpacecraftEstimation of Distribution AlgorithmsAstrodynamicsOrbital MechanicsOne particular kind of evolutionary algorithms known as Estimation of Distribution Algorithms (EDAs) has gained the attention of the aerospace industry for its ability to solve nonlinear and complicated problems, particularly in the optimization of space trajectories during on-orbit operations of satellites. This article describes an effective method for optimizing the trajectory of a spacecraft using an evolutionary approach based on EDAs, incorporated with fitness landscape analysis (FLA). The approach utilizes flexible operators that are paired with seeding and selection mechanisms of EDAs. Initially, the orbit transfer problem is mathematically modeled and the objectives and constraints are identified. The landscape feature of the search space is analyzed via the dispersion metric to measure the modality and ruggedness of the search domain. The obtained information are used as feedback in developing adaptive operators for truncation factor and constraints separation threshold of the employed EDA. A framework for spacecraft trajectory optimization has been presented where the dispersion value for a space mission is estimated using a k-nearest neighbors (k-NN) algorithm. The suggested method is used to solve several problems related to low-thrust orbit transfer of satellites in Earth’s orbit. Results demonstrate that the suggested framework for trajectory design and optimization of space transfers is effective enough to offer fuel-efficient and energy-efficient maneuvers for different thrust levels of the propulsion system. Moreover, the performance of the proposed approach is evaluated against non-adaptive EDA and other advanced evolutionary algorithms. The obtained results certify that the proposed adaptive evolutionary approach is superior in identifying feasible minimum-fuel and minimum-energy transfer trajectories.BEAZ Bizkaia, 3/12/DP/2021/00150; SPRI Group, Ekintzaile Program EK-00112-2021202320232023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/20.500.11824/1719reponame:BIRD. BCAM's Institutional Repository Datainstname:Basque Center for Applied Mathematics (BCAM)Ingléshttps://www.sciencedirect.com/science/article/abs/pii/S156849462300491Xinfo:eu-repo/grantAgreement/MINECO//SEV-2017-0718info:eu-repo/grantAgreement/Gobierno Vasco/BERC/BERC.2022-2025Reconocimiento-NoComercial-CompartirIgual 3.0 Españahttp://creativecommons.org/licenses/by-nc-sa/3.0/es/info:eu-repo/semantics/openAccessoai:bird.bcamath.org:20.500.11824/17192026-06-19T12:47:47Z
dc.title.none.fl_str_mv Robust Estimation of Distribution Algorithms via Fitness Landscape Analysis for Optimal Low-Thrust Orbital Maneuvers
title Robust Estimation of Distribution Algorithms via Fitness Landscape Analysis for Optimal Low-Thrust Orbital Maneuvers
spellingShingle Robust Estimation of Distribution Algorithms via Fitness Landscape Analysis for Optimal Low-Thrust Orbital Maneuvers
Shirazi, A.
Aerospace Engineering
Evolutionary Algorithm
Optimization
Spacecraft
Estimation of Distribution Algorithms
Astrodynamics
Orbital Mechanics
title_short Robust Estimation of Distribution Algorithms via Fitness Landscape Analysis for Optimal Low-Thrust Orbital Maneuvers
title_full Robust Estimation of Distribution Algorithms via Fitness Landscape Analysis for Optimal Low-Thrust Orbital Maneuvers
title_fullStr Robust Estimation of Distribution Algorithms via Fitness Landscape Analysis for Optimal Low-Thrust Orbital Maneuvers
title_full_unstemmed Robust Estimation of Distribution Algorithms via Fitness Landscape Analysis for Optimal Low-Thrust Orbital Maneuvers
title_sort Robust Estimation of Distribution Algorithms via Fitness Landscape Analysis for Optimal Low-Thrust Orbital Maneuvers
dc.creator.none.fl_str_mv Shirazi, A.
author Shirazi, A.
author_facet Shirazi, A.
author_role author
dc.subject.none.fl_str_mv Aerospace Engineering
Evolutionary Algorithm
Optimization
Spacecraft
Estimation of Distribution Algorithms
Astrodynamics
Orbital Mechanics
topic Aerospace Engineering
Evolutionary Algorithm
Optimization
Spacecraft
Estimation of Distribution Algorithms
Astrodynamics
Orbital Mechanics
description One particular kind of evolutionary algorithms known as Estimation of Distribution Algorithms (EDAs) has gained the attention of the aerospace industry for its ability to solve nonlinear and complicated problems, particularly in the optimization of space trajectories during on-orbit operations of satellites. This article describes an effective method for optimizing the trajectory of a spacecraft using an evolutionary approach based on EDAs, incorporated with fitness landscape analysis (FLA). The approach utilizes flexible operators that are paired with seeding and selection mechanisms of EDAs. Initially, the orbit transfer problem is mathematically modeled and the objectives and constraints are identified. The landscape feature of the search space is analyzed via the dispersion metric to measure the modality and ruggedness of the search domain. The obtained information are used as feedback in developing adaptive operators for truncation factor and constraints separation threshold of the employed EDA. A framework for spacecraft trajectory optimization has been presented where the dispersion value for a space mission is estimated using a k-nearest neighbors (k-NN) algorithm. The suggested method is used to solve several problems related to low-thrust orbit transfer of satellites in Earth’s orbit. Results demonstrate that the suggested framework for trajectory design and optimization of space transfers is effective enough to offer fuel-efficient and energy-efficient maneuvers for different thrust levels of the propulsion system. Moreover, the performance of the proposed approach is evaluated against non-adaptive EDA and other advanced evolutionary algorithms. The obtained results certify that the proposed adaptive evolutionary approach is superior in identifying feasible minimum-fuel and minimum-energy transfer trajectories.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.11824/1719
url http://hdl.handle.net/20.500.11824/1719
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://www.sciencedirect.com/science/article/abs/pii/S156849462300491X
info:eu-repo/grantAgreement/MINECO//SEV-2017-0718
info:eu-repo/grantAgreement/Gobierno Vasco/BERC/BERC.2022-2025
dc.rights.none.fl_str_mv Reconocimiento-NoComercial-CompartirIgual 3.0 España
http://creativecommons.org/licenses/by-nc-sa/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Reconocimiento-NoComercial-CompartirIgual 3.0 España
http://creativecommons.org/licenses/by-nc-sa/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:BIRD. BCAM's Institutional Repository Data
instname:Basque Center for Applied Mathematics (BCAM)
instname_str Basque Center for Applied Mathematics (BCAM)
reponame_str BIRD. BCAM's Institutional Repository Data
collection BIRD. BCAM's Institutional Repository Data
repository.name.fl_str_mv
repository.mail.fl_str_mv
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