Structured-chromosome GA optimisation for satellite tracking

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

Abstract

This paper presents a novel optimisation approach, called Structured-Chromosome Genetic Algorithm (SCGA), that addresses the issue of handling variable-size design space optimisation problems. This is based on variants of standard genetic operators able to handle structured search spaces. The potential of the presented methodology is shown by solving the problem of defining observation campaigns for tracking space objects from a network of tracking stations. The presented approach aims at supporting the space sector in response to the constantly increasing population size in the around-Earth environment. The test case consists in finding the observation scheduling that minimises the uncertainty in the final state estimation of a very low Earth satellite operating in a highly perturbed dynamical environment. This is evaluated by coupling the optimiser with an estimation routine based on a sequential filtering approach that estimates the satellite state distribution conditional on received indirect measurements. The solutions found by employing SCGA are finally compared to the ones achieved using more traditional approaches. Namely, the problem has been reformulated to be faced using standard Genetic Algorithm and another variable-size optimiser, the "Hidden-genes" Genetic Algorithm variant.
LanguageEnglish
Title of host publicationGECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference Companion
Place of PublicationPrague, Czech Republic
Pages1955-1963
Number of pages9
DOIs
Publication statusPublished - 30 Sep 2019

Fingerprint

Chromosomes
Genetic algorithms
Earth (planet)
State estimation
Genes
Scheduling
Satellites

Keywords

  • optimisation
  • genetic algorithm (GA)
  • scheduling optimization
  • satellite observation

Cite this

Gentile, L., Greco, C., Minisci, E., Bartz-Beielstein, T., & Vasile, M. (2019). Structured-chromosome GA optimisation for satellite tracking. In GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 1955-1963). Prague, Czech Republic. https://doi.org/10.1145/3319619.3326841
Gentile, Lorenzo ; Greco, Cristian ; Minisci, Edmondo ; Bartz-Beielstein, Thomas ; Vasile, Massimiliano. / Structured-chromosome GA optimisation for satellite tracking. GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference Companion. Prague, Czech Republic, 2019. pp. 1955-1963
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Gentile, L, Greco, C, Minisci, E, Bartz-Beielstein, T & Vasile, M 2019, Structured-chromosome GA optimisation for satellite tracking. in GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference Companion. Prague, Czech Republic, pp. 1955-1963. https://doi.org/10.1145/3319619.3326841

Structured-chromosome GA optimisation for satellite tracking. / Gentile, Lorenzo; Greco, Cristian; Minisci, Edmondo; Bartz-Beielstein, Thomas; Vasile, Massimiliano.

GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference Companion. Prague, Czech Republic, 2019. p. 1955-1963.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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N2 - This paper presents a novel optimisation approach, called Structured-Chromosome Genetic Algorithm (SCGA), that addresses the issue of handling variable-size design space optimisation problems. This is based on variants of standard genetic operators able to handle structured search spaces. The potential of the presented methodology is shown by solving the problem of defining observation campaigns for tracking space objects from a network of tracking stations. The presented approach aims at supporting the space sector in response to the constantly increasing population size in the around-Earth environment. The test case consists in finding the observation scheduling that minimises the uncertainty in the final state estimation of a very low Earth satellite operating in a highly perturbed dynamical environment. This is evaluated by coupling the optimiser with an estimation routine based on a sequential filtering approach that estimates the satellite state distribution conditional on received indirect measurements. The solutions found by employing SCGA are finally compared to the ones achieved using more traditional approaches. Namely, the problem has been reformulated to be faced using standard Genetic Algorithm and another variable-size optimiser, the "Hidden-genes" Genetic Algorithm variant.

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Gentile L, Greco C, Minisci E, Bartz-Beielstein T, Vasile M. Structured-chromosome GA optimisation for satellite tracking. In GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference Companion. Prague, Czech Republic. 2019. p. 1955-1963 https://doi.org/10.1145/3319619.3326841