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

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.

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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Contributors

Author: Alexander Wittig

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