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Hybrid Behavioral-Based Multiobjective Space Trajectory Optimization

  • Massimiliano Vasile
Part of the Studies in Computational Intelligence book series (SCI, volume 171)

Abstract

In this chapter we present a hybridization of a stochastic based search approach for multi-objective optimization with a deterministic domain decomposition of the solution space. Prior to the presentation of the algorithm we introduce a general formulation of the optimization problem that is suitable to describe both single and multi-objective problems. The stochastic approach, based on behaviorism, combined with the decomposition of the solutions pace was tested on a set of standard multi-objective optimization problems and on a simple but representative case of space trajectory design.

Keywords

Differential Evolution Pareto Front Multiobjective Optimization Domain Decomposition Dominance Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Chipperfield, A.J., Fleming, P.J., Pohlheim, H., Fonseca, C.M.: Genetic Algorithm Toolbox User’s Guide, ACSE Research Report No. 512, University of Sheffield (1994)Google Scholar
  2. 2.
    Horst, R., Tuy, H.: Global optimization: deterministic approaches, 3rd edn. Springer, Berlin (1996)zbMATHGoogle Scholar
  3. 3.
    Stephens, C.P., Baritompa, W.: Global optimization requires global information. Journal of Optmization Theory and Applications 96, 575–588 (1998)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Storn, R., Price, K.: Differential Evolution – a Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization 11(4), 341–359 (1997)zbMATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Torn, A., Zilinskas, A.: Global Optimization. Springer, Berlin (1987)Google Scholar
  6. 6.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
  7. 7.
    Perttunen, C.D., Stuckman, B.E.: Lipschitzian Optimization Without the Lipschitz Constant. JOTA 79(1), 157–181 (1993)zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Price, K.V., Storn, R.M.: Jouni A. Lampinen. Differential Evolution: A Practical Approach to Global Optimization, 1st edn. Springer, Heidelberg (December 22, 2005)Google Scholar
  9. 9.
    Deb, K., Pratap, A., Meyarivan, T.: Fast elitist multi-objective genetic algorithm: NGA-II. KanGAL Report No. 200001 (2000)Google Scholar
  10. 10.
    Deb, K., Pratap, A., Meyarivan, T.: Constrained test problems for multi-objective evolutionary optimization. KanGAL Report No. 200002 (2002)Google Scholar
  11. 11.
    Sierra, M.R., Coello, C.A.C.: A Study of Techniques to Improve the Efficiency of a Multi-Objective Particle Swarm Optimizer. Evolutionary Computation in Dynamic and Uncertain Environments, 269–296 (2007)Google Scholar
  12. 12.
    Coello, C.A.C., Lamont, G., Van Veldhuizen, D.: Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd edn. Springer, New York (2007)zbMATHGoogle Scholar
  13. 13.
    Gage, P.J., Braun, R.D., Kroo, I.M.: Interplanetary trajectory optimisation using a genetic algorithm. Journal of the Astronautical Sciences 43(1), 59–75 (1995)Google Scholar
  14. 14.
    Gurfil, P., Kasdin, N.J.: Niching genetic algorithms-based characterization of geocentric orbits in the 3D elliptic restricted three-body problem. Computer Methods in Applied Mechanics and Engineering 191(49-50), 5673–5696 (2002)CrossRefGoogle Scholar
  15. 15.
    Hartmann, J.W., Coverstone-Carrol, V.L., Williams, S.N.: Optimal Interplanetary Spacecraft Trajectories via Pareto Genetic Algorithm. Journal of the Astronautical Sciences 46(3) (1998)Google Scholar
  16. 16.
    Rauwolf, G., Coverstone-Carroll, V.: Near-optimal low-thrust orbit transfers generated by a genetic algorithm. Journal of Spacecraft and Rockets 33(6), 859–862 (1996)CrossRefGoogle Scholar
  17. 17.
    Vasile, M.: Combining Evolution Programs and Gradient Methods for WSB Transfer Optimisation. In: Operational Research in Space & Air, vol. 79, Book Series in Applied Optimization Kluwer Academy Press (2003) ISBN 1-4020-1218-7Google Scholar
  18. 18.
    De Pascale, P., Vasile, M.: Preliminary Design of Low-Thrust Multiple Gravity Assist Trajectories. Journal of Spacecraft and Rockets 43(5), 1065–1076 (2006)CrossRefGoogle Scholar
  19. 19.
    Dachwald, B.: Optimization of interplanetary solar sailcraft trajectories using evolutionary neurocontrol. Journal of Guidance, and Dynamics (January/ February 2004)Google Scholar
  20. 20.
    Myatt, D.R., Becerra, V.M., Nasuto, S.J., Bishop, J.M.: Advanced Global Optimization Tools for Mission Analysis and Design. Final Rept. ESA Ariadna ITT AO4532/18138/04/NL/MV, Call03/4101 (2004)Google Scholar
  21. 21.
    Di Lizia, P., Radice, G.: Advanced Global Optimisation Tools for Mission Analysis and Design. European Space Agency, the Advanced Concepts Team, Ariadna Final Report 03-4101b (2004)Google Scholar
  22. 22.
    Dam, R.E., van Husslage, B.G.M., den Hertog, D., Melissen, J.B.M.: Maximin Latin hypercube designs in two dimensions. Operations Research 55, 158–169 (2007)CrossRefMathSciNetGoogle Scholar
  23. 23.
    Battin, R.H.: An Introduction to the Mathematics and Methods of Astrodynamics, Revised Edition (Aiaa Education Series). AIAA (American Institute of Aeronautics & Ast; Revised edition (1999) ISBN-13: 978-1563473425Google Scholar
  24. 24.
    Vasile, M.: A Behavioral-based Meta-heuristic for Robust Global Trajectory Optimization. In: IEEE Congress on Evolutionary Computing (CEC 2007) Proceedings, pp. 2056–2063 (2007)Google Scholar
  25. 25.
    Vasile, M., Locatelli, M.: A hybrid multiagent approach for global trajectory optimization. Journal of Glaobal Optimization (accepted) (to appear, 2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Massimiliano Vasile
    • 1
  1. 1.Department of Aerospace EngineeringUniversity of GlasgowGlasgowUK

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