Search space pruning and global optimization of multiple gravity assist trajectories with deep space manoeuvres

Becerra, V. M., Nasuto, S. J. ORCID: https://orcid.org/0000-0001-9414-9049, Anderson, J., Ceriotti, M. and Bombardelli, C. (2007) Search space pruning and global optimization of multiple gravity assist trajectories with deep space manoeuvres. In: 2007 IEEE Congress on Evolutionary Computation, Vols 1-10, Proceedings. IEEE Congress on Evolutionary Computation. IEEE, New York, pp. 957-964. ISBN 9781424413393

Abstract/Summary

This paper deals with the design of optimal multiple gravity assist trajectories with deep space manoeuvres. A pruning method which considers the sequential nature of the problem is presented. The method locates feasible vectors using local optimization and applies a clustering algorithm to find reduced bounding boxes which can be used in a subsequent optimization step. Since multiple local minima remain within the pruned search space, the use of a global optimization method, such as Differential Evolution, is suggested for finding solutions which are likely to be close to the global optimum. Two case studies are presented.