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Taboo Evolutionary Programming Approach to Optimal Transfer from Earth to Mars

  • M. Mutyalarao
  • A. Sabarinath
  • M. Xavier James Raj
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7077)

Abstract

Taboo evolutionary programming (TEP) is a novel evolutionary programming technique found extensive usage in the present decade. The algorithm can be implemented in many complex problems in science and technology to find the optimum solutions with constraints. In this paper, we studied a two-point boundary value problem such as Lambert conic determination to find out the optimum impulsive requirements for Earth to Mars transfer from a Geo stationary Transfer Orbit (GTO). The TEP results were compared with Genetic Algorithm (GA) and found that the TEP gives better results.

Keywords

Evolutionary Programming Genetic algorithm optimum flyby orbiter ballistic trajectory 

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References

  1. 1.
    Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Trans. Evol. Comput. 3, 82–102 (1999)CrossRefGoogle Scholar
  2. 2.
    Cvijovic, D., Klinowski, J.: Taboo search: an approach to the multiple minima problem. Science 267, 664–666 (1995)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Glover, F.: Tabu search- part I. ORSA Journal of Computing 1, 190–206 (1989)CrossRefzbMATHGoogle Scholar
  4. 4.
    Glover, F.: Tabu search-part II. ORSA Journal of Computing 2, 4–32 (1990)CrossRefzbMATHGoogle Scholar
  5. 5.
    Rajesh, J., Jayaraman, V.K., Kulkarni, B.D.: Taboo search algorithm for continuous function optimization. Trans. IChemE 78, Part A (2000)Google Scholar
  6. 6.
    Ji, M., Klinowski, J.: Taboo evolutionary programming: a new method of global optimization. Proc. R. Soc. A 462, 3613–3627 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Ji, M., Klinowski, J.: Convergence of taboo search in continuous global optimization. Proc. R. Soc. A 462, 2077–2084 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Ji, M., Tang, H., Guo, J.: A single-point mutation evolutionary programming. Inf. Process. Lett. 90, 293–299 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Minhazul Islam, S., Das, S., Ghosh, S., Roy, S., Suganthan, P.N.: An Adaptive Differential Evolution Algorithm with Novel Mutation and Crossover Strategies for Global Numerical Optimization. accepted by IEEE Trans. on SMC-B (2011)Google Scholar
  10. 10.
    Vallado, D.A.: Fundamentals of astrodynamics and applications, 2nd edn. Kluwer Academic Publishers, London (2001)zbMATHGoogle Scholar
  11. 11.
    Battin, R.H.: An introduction to the mathematics and methods of astrodynamics. AIAA education series (1987)Google Scholar
  12. 12.
    Mallipeddi, R., Mallipeddi, S., Suganthan, P.N.: Ensemble strategies with adaptive evolutionary programming. Information Sciences 180(9), 1571–1581 (2010)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • M. Mutyalarao
    • 1
  • A. Sabarinath
    • 1
  • M. Xavier James Raj
    • 1
  1. 1.Applied Mathematics DivisionVikram Sarabhai Space CentreTrivandrumIndia

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