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On the use of GPUs for massively parallel optimization of low-thrust trajectories

On the use of GPUs for massively parallel optimization of low-thrust trajectories
On the use of GPUs for massively parallel optimization of low-thrust trajectories
The optimization of low-thrust trajectories is a difficult task. While techniques such as Sims-Flanagan transcription give good results for short transfer arcs with at most a few revolutions, solving the low-thrust problem for orbits with large numbers of revolutions is much more difficult. Adding to the difficulty of the problem is that typically such orbits are formulated as a multi-objective optimization problem, providing a trade-off between fuel consumption and flight time. In this work we propose to leverage the power of modern GPU processors to implement a massively parallel evolutionary optimization algorithm. Modern GPUs are capable of running thousands of computation threads in parallel, allowing for very efficient evaluation of the fitness function over a large population. A core component of this algorithm is a fast massively parallel numerical integrator capable of propagating thousands of initial conditions in parallel on the GPU. Several evolutionary optimization algorithms are analyzed for their suitability for large population size. An example of how this technique can be applied to low-thrust optimization in the targeting of the Moon is given.
GPU, ODE integration, evolutionary, global optimization, low thrust, parallel
Wittig, Alexander
3a140128-b118-4b8c-9856-a0d4f390b201
Wase, Viktor
3fd7ce73-eae1-4c85-bdf3-30d2772fec90
Izzo, Dario
f2f4a956-4193-4150-9734-2afc0544be6b
Wittig, Alexander
3a140128-b118-4b8c-9856-a0d4f390b201
Wase, Viktor
3fd7ce73-eae1-4c85-bdf3-30d2772fec90
Izzo, Dario
f2f4a956-4193-4150-9734-2afc0544be6b

Wittig, Alexander, Wase, Viktor and Izzo, Dario (2016) On the use of GPUs for massively parallel optimization of low-thrust trajectories. The 6th International Conference on Astrodynamics Tools and Techniques (ICATT), Darmstadt, Germany. 14 - 17 Mar 2016.

Record type: Conference or Workshop Item (Paper)

Abstract

The optimization of low-thrust trajectories is a difficult task. While techniques such as Sims-Flanagan transcription give good results for short transfer arcs with at most a few revolutions, solving the low-thrust problem for orbits with large numbers of revolutions is much more difficult. Adding to the difficulty of the problem is that typically such orbits are formulated as a multi-objective optimization problem, providing a trade-off between fuel consumption and flight time. In this work we propose to leverage the power of modern GPU processors to implement a massively parallel evolutionary optimization algorithm. Modern GPUs are capable of running thousands of computation threads in parallel, allowing for very efficient evaluation of the fitness function over a large population. A core component of this algorithm is a fast massively parallel numerical integrator capable of propagating thousands of initial conditions in parallel on the GPU. Several evolutionary optimization algorithms are analyzed for their suitability for large population size. An example of how this technique can be applied to low-thrust optimization in the targeting of the Moon is given.

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More information

Published date: March 2016
Venue - Dates: The 6th International Conference on Astrodynamics Tools and Techniques (ICATT), Darmstadt, Germany, 2016-03-14 - 2016-03-17
Keywords: GPU, ODE integration, evolutionary, global optimization, low thrust, parallel

Identifiers

Local EPrints ID: 420467
URI: http://eprints.soton.ac.uk/id/eprint/420467
PURE UUID: ef35d3e0-38e5-4397-8404-66dce62dcde8
ORCID for Alexander Wittig: ORCID iD orcid.org/0000-0002-4594-0368

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Date deposited: 08 May 2018 16:30
Last modified: 16 Mar 2024 04:30

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Contributors

Author: Viktor Wase
Author: Dario Izzo

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