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

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.

Conference

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

Fingerprint

Global optimization
Gravitation
Trajectories
Clustering algorithms

Keywords

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

Cite this

Becerra, V. M., Nasuto, S. J., Anderson, J. D., Ceriotti, M., & Bombardelli, C. (2007). Search space pruning and global optimization of multiple gravity assist trajectories with deep space manoeuvers. Paper presented at IEEE Congress on Evolutionary Computation (CEC), Singapore, .
Becerra, Victor M. ; Nasuto, Slawomir J. ; Anderson, James D. ; Ceriotti, M. ; Bombardelli, Claudio. / Search space pruning and global optimization of multiple gravity assist trajectories with deep space manoeuvers. Paper presented at IEEE Congress on Evolutionary Computation (CEC), Singapore, .
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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.",
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note = "IEEE Congress on Evolutionary Computation (CEC) ; Conference date: 25-09-2007 Through 28-09-2007",

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Becerra, VM, Nasuto, SJ, Anderson, JD, Ceriotti, M & Bombardelli, C 2007, 'Search space pruning and global optimization of multiple gravity assist trajectories with deep space manoeuvers' Paper presented at IEEE Congress on Evolutionary Computation (CEC), Singapore, 25/09/07 - 28/09/07, .

Search space pruning and global optimization of multiple gravity assist trajectories with deep space manoeuvers. / Becerra, Victor M.; Nasuto, Slawomir J.; Anderson, James D.; Ceriotti, M.; Bombardelli, Claudio.

2007. Paper presented at IEEE Congress on Evolutionary Computation (CEC), Singapore, .

Research output: Contribution to conferencePaper

TY - CONF

T1 - Search space pruning and global optimization of multiple gravity assist trajectories with deep space manoeuvers

AU - Becerra, Victor M.

AU - Nasuto, Slawomir J.

AU - Anderson, James D.

AU - Ceriotti, M.

AU - Bombardelli, Claudio

PY - 2007/9/25

Y1 - 2007/9/25

N2 - 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.

AB - 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.

KW - optimal multiple gravity assist trajectories

KW - deep space manoeuvres

KW - local optimization

KW - global optimization method

KW - differential evolution

UR - http://cec2007.nus.edu.sg/

M3 - Paper

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Becerra VM, Nasuto SJ, Anderson JD, Ceriotti M, Bombardelli C. Search space pruning and global optimization of multiple gravity assist trajectories with deep space manoeuvers. 2007. Paper presented at IEEE Congress on Evolutionary Computation (CEC), Singapore, .