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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.
Original language | English |
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Title of host publication | GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference Companion |
Place of Publication | Prague, Czech Republic |
Pages | 1955-1963 |
Number of pages | 9 |
DOIs | |
Publication status | Published - 30 Sept 2019 |
Keywords
- optimisation
- genetic algorithm (GA)
- scheduling optimization
- satellite observation
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Dive into the research topics of 'Structured-chromosome GA optimisation for satellite tracking'. Together they form a unique fingerprint.Projects
- 1 Finished
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Uncertainty Treatment and OPtimisation in Aerospace Engineering (UTOPIAE) (H2020 MCSA ETN)
Vasile, M. (Principal Investigator), Akartunali, K. (Co-investigator), Maddock, C. (Co-investigator), Minisci, E. (Co-investigator) & Revie, M. (Co-investigator)
European Commission - Horizon Europe + H2020
1/01/17 → 31/12/20
Project: Research