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

Victor M. Becerra, Slawomir J. Nasuto, James D. Anderson, M. Ceriotti, Claudio Bombardelli

Research output: Contribution to conferencePaper

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Abstract

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.
Original languageEnglish
Publication statusPublished - 25 Sep 2007
EventIEEE Congress on Evolutionary Computation (CEC) - Singapore
Duration: 25 Sep 200728 Sep 2007

Conference

ConferenceIEEE Congress on Evolutionary Computation (CEC)
CitySingapore
Period25/09/0728/09/07

Keywords

  • optimal multiple gravity assist trajectories
  • deep space manoeuvres
  • local optimization
  • global optimization method
  • differential evolution

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