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International Journal of Aerospace Engineering
Volume 2013, Article ID 145369, 11 pages
Research Article

Autonomous Planning of Multigravity-Assist Trajectories with Deep Space Maneuvers Using a Differential Evolution Approach

Mechanical Engineering-Engineering Mechanics Department, Michigan Technological University, 815 R.L. Smith Building, 1400 Townsend Dr., Houghton, Mine 49931-1295, USA

Received 2 February 2013; Revised 21 June 2013; Accepted 7 July 2013

Academic Editor: Paolo Tortora

Copyright © 2013 Ossama Abdelkhalik. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.