Search space pruning and global optimization of multiple gravity assist trajectories with deep space manoeuvresBecerra, V. M., Nasuto, S. J., 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 Full text not archived in this repository. It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. Abstract/SummaryThis 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.
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