Abstract
The Covariance Matrix Adaptation-Evolutionary Strategy (CMA-ES) method provides a high-quality estimate of the control solution for an unconstrained satellite reorientation problem, and rapid, useful guesses needed for high-fidelity methods that can solve time-optimal reorientation problems with multiple path constraints. The CMA-ES algorithm offers two significant advantages over heuristic methods such as Particle Swarm or Bacteria Foraging Optimisation: it builds an approximation to the covariance matrix for the cost function, and uses that to determine a direction of maximum likelihood for the search, reducing the chance of stagnation; and it achieves second-order, quasi-Newton convergence behaviour.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 185-192 |
| Number of pages | 8 |
| Journal | Acta Astronautica |
| Volume | 103 |
| DOIs | |
| State | Published - Jan 1 2014 |
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All Science Journal Classification (ASJC) codes
- Aerospace Engineering
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Maximum-likelihood estimation optimizer for constrained, time-optimal satellite reorientation. / Melton, Robert Graham.
In: Acta Astronautica, Vol. 103, 01.01.2014, p. 185-192.Research output: Contribution to journal › Article
TY - JOUR
T1 - Maximum-likelihood estimation optimizer for constrained, time-optimal satellite reorientation
AU - Melton, Robert Graham
PY - 2014/1/1
Y1 - 2014/1/1
N2 - The Covariance Matrix Adaptation-Evolutionary Strategy (CMA-ES) method provides a high-quality estimate of the control solution for an unconstrained satellite reorientation problem, and rapid, useful guesses needed for high-fidelity methods that can solve time-optimal reorientation problems with multiple path constraints. The CMA-ES algorithm offers two significant advantages over heuristic methods such as Particle Swarm or Bacteria Foraging Optimisation: it builds an approximation to the covariance matrix for the cost function, and uses that to determine a direction of maximum likelihood for the search, reducing the chance of stagnation; and it achieves second-order, quasi-Newton convergence behaviour.
AB - The Covariance Matrix Adaptation-Evolutionary Strategy (CMA-ES) method provides a high-quality estimate of the control solution for an unconstrained satellite reorientation problem, and rapid, useful guesses needed for high-fidelity methods that can solve time-optimal reorientation problems with multiple path constraints. The CMA-ES algorithm offers two significant advantages over heuristic methods such as Particle Swarm or Bacteria Foraging Optimisation: it builds an approximation to the covariance matrix for the cost function, and uses that to determine a direction of maximum likelihood for the search, reducing the chance of stagnation; and it achieves second-order, quasi-Newton convergence behaviour.
UR - http://www.scopus.com/inward/record.url?scp=84904966045&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84904966045&partnerID=8YFLogxK
U2 - 10.1016/j.actaastro.2014.06.032
DO - 10.1016/j.actaastro.2014.06.032
M3 - Article
AN - SCOPUS:84904966045
VL - 103
SP - 185
EP - 192
JO - Acta Astronautica
JF - Acta Astronautica
SN - 0094-5765
ER -