Autonomous planning of multigravity-assist trajectories with deep space maneuvers using a differential evolution approach
Document Type
Article
Publication Date
9-23-2013
Abstract
The biologically inspired concept of hidden genes has been recently introduced in genetic algorithms to solve optimization problems where the number of design variables is variable. In multigravity-assist trajectories, the hidden genes genetic algorithms demonstrated success in searching for the optimal number of swing-bys and the optimal number of deep space maneuvers. Previous investigations in the literature for multigravity-assist trajectory planning problems show that the standard differential evolution is more effective than the standard genetic algorithms. This paper extends the concept of hidden genes to differential evolution. The hidden genes differential evolution is implemented in optimizing multigravity-assist space trajectories. Case studies are conducted, and comparisons to the hidden genes genetic algorithms are presented in this paper. © 2013 Ossama Abdelkhalik.
Publication Title
International Journal of Aerospace Engineering
Recommended Citation
Abdelkhalik, O.
(2013).
Autonomous planning of multigravity-assist trajectories with deep space maneuvers using a differential evolution approach.
International Journal of Aerospace Engineering.
http://doi.org/10.1155/2013/145369
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/12683