Study on Application of Multi-Objective Differential Evolution Algorithm in Space Rendezvous

  • Lei Peng
  • Guangming Dai
  • Fangjie Chen
  • Fei Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4683)


As the development of human missions and space station, space rendezvous and docking technology is the key to modern space exploration. There are a lot of multi-objective optimization problems in aerospace field. At present, polymerization technology is often used to change multi-objective to single objective. This method makes the problem easier but gives one solution only which is not suitable for project application. In this paper, we introduce an extension of DE(SMODE) to cope with the spacecraft rendezvous problem. The experiment results indicate that SMODE is successful to locate the real Pareto front for the spacecraft rendezvous problem. Also, the effect of PopSize–population size and Max_gen–maximum number of generations of SMODE is studied.


Differential Evolution Multiobjective Optimization Nondominated Solution Trial Vector Rendezvous Problem 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Jun, L.T.: Spacecraft Dynamics. Press of Harbin Institute of Technology (2003)Google Scholar
  2. 2.
    Wang, H., Tang, G.: Study on Appliaction of Genetic Algorithm in Spacecraft Optimal Rendezvous. Journal of Astronautics Control 1, 16–21 (2003)Google Scholar
  3. 3.
    Wang, S., Zhu, K.-j., Dai, J.-h., Ren, X.: Solving orbital transformation problems based on EA. Journal of Astronautics 23(1), 73–75 (2002)Google Scholar
  4. 4.
    Tang, Y., Chen, S., Xu, M., Wan, Z.: A genetic algorithm (GA) method of orbit interception with finite thrust. Journal of Northwestern Polytechnical University 23(5), 671–674 (2005)Google Scholar
  5. 5.
    Peng, L., Wu, Y., Hu, H.: Solving spacecraft rendezvous problems based on DE. In: The third Chinese astronautics institute deep-space committee academic conference, pp. 81–86 (2006)Google Scholar
  6. 6.
    Storn, R., Price, K.: Differential evolution-A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11, 341–359 (1997)zbMATHCrossRefGoogle Scholar
  7. 7.
    Abbass, H.A.: The self-adaptive pareto differential evolution algorithm. In: CEC 2002. Congress on Evolutionary Computation, vol. 1, pp. 831–836. IEEE Service Center, Piscataway, New Jersey (2002)Google Scholar
  8. 8.
    Xue, F., Sanderson, A.C., Graves, R.J.: Pareto-based multi-objective differential evolution. In: CEC 2003. Proceedings of the 2003 Congress on Evolutionary Computation, Canberra, Australia, vol. 2, pp. 862–869. IEEE Press, NJ, New York (2003)Google Scholar
  9. 9.
    Robi, T., Filipic, B.: DEMO: Differential Evolution for Multi-objective Optimization. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 520–533. Springer, Heidelberg (2005)Google Scholar
  10. 10.
    Babu, B.V., Jehan, M.M.L.: Differential Evolution for Multi-Objective Optimization. In: CEC 2003, Canberra, Australia, vol. 4, pp. 2696–2703 (December 2003)Google Scholar
  11. 11.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGACII. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)CrossRefGoogle Scholar
  12. 12.
    Angira, R., Babu, B.V.: Non-dominated sorting differential evolution (NSDE):an extension of differential evolution for multi-objective optimization. In: IICAI-05. 2nd Indian international conference artificial intellgence, pp. 1428–1443 (2005)Google Scholar
  13. 13.
    Madavan, N.K.: Multiobjective optimization using a Pareto differential evolution approach. In: Proc. of IEEE Congress on Evolutionary Computation, pp. 1145–1150. IEEE Computer Society Press, Los Alamitos (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Lei Peng
    • 1
  • Guangming Dai
    • 1
  • Fangjie Chen
    • 1
  • Fei Liu
    • 1
  1. 1.School of Computer Science, China University of Geosciences, Wuhan 430074P.R. China

Personalised recommendations