Stochastic Models and Optimization Algorithms for Decision Support in Spacecraft Control Systems Preliminary Design

Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 283)


Technological and command-programming control contours of spacecraft are modelled with Markov chains. These models are used for the preliminary design of spacecraft control system effective structure with the use of special DSS. Corresponding optimization problems with algorithmically given functions of mixed variables are solved with a special stochastic algorithm called self-configuring genetic algorithm that requires no settings determination and parameter tuning. The high performance of the suggested algorithm is proved by the solving real problems of the control contours structure preliminary design.


Spacecraft control contours modelling Markov chains  Effective variant choice Complex optimization problems  Self-configuring genetic algorithm Island model 



The research is supported through the Governmental contracts No 16.740.11.0742 and No 11.519.11.4002. The authors are deeply grateful to Dr. Linda Ott, a professor at the Technological University of Michigan, for her invaluable help in improving the text of the article.


  1. 1.
    De Jong, K. A., Spears, W.: On the virtues of parameterized uniform crossover. In: Belew, R. K., Booker, L. B. (eds.) Proceedings of the 4th International Conference on Genetic Algorithms, pp. 230–236. Morgan Kaufmann (1991)Google Scholar
  2. 2.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer-Verlag, Berlin, Heidelberg (2003)Google Scholar
  3. 3.
    Eiben, A. E., Hinterding, R., Michalewicz, Z.: Parameter control in evolutionary algorithms. IEEE Trans. Evol. Comput 3(2), 124–141 (1999) (IEEE press piscataway, NJ)Google Scholar
  4. 4.
    Finck, S., Hansen, N., Ros, R., Auger, A.: Real-parameter black-box optimization benchmarking 2009: presentation of the noiseless functions. Technical Report 2009/20, Research center PPE (2009)Google Scholar
  5. 5.
    Gomez, J.: Self-adaptation of operator rates in evolutionary algorithms. In: Deb, K., et al. (eds.) GECCO 2004, LNCS 3102, pp. 1162–1173. Springer, Heidelberg (2004)Google Scholar
  6. 6.
    Haupt, R.L., Haupt, S.E.: Practical Genetic Algorithms. Wiley, Hoboken (2004)Google Scholar
  7. 7.
    Lardeux, F., Goëffon, A.: A dynamic island-based genetic algorithms framework. In: Simulated evolution and learning, lecture notes in computer science 6457, pp. 156–165. Springer, Heidelberg (2010)Google Scholar
  8. 8.
    Semenkin, E. S., Semenkina, M. E.: Application of genetic algorithm with modified uniform recombination operator for automated implementation of intellectual information technologies. In: Vestnik. Scientific Journal of the Siberian State Aerospace University named after academician M. F. Reshetnev. - Issue 3 (16), 27–32. (In Russian, abstract in English). SibSAU press, Krasnoyarsk (2007)Google Scholar
  9. 9.
    Semenkin, E., Semenkina, M.: Spacecrafts’ control systems effective variants choice with self-configuring genetic algorithm. In: Ferrier, J.-L., Bernard, A., Gusikhin, O., Madani, K. (eds), Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics, vol. 1, pp. 84–93 (2012)Google Scholar
  10. 10.
    Semenkin, E., Semenkina, M.: Self-configuring genetic algorithm with modified uniform crossover operator. In: Tan, Y., Shi, Y., Ji, Z. (Eds.): Advances in Swarm Intelligence, ICSI 2012, Part I, LNCS 7331, pp. 414–421. Springer, Heidelberg (2012)Google Scholar
  11. 11.
    Syswerda, G.: Uniform crossover in genetic algorithms. In: Schaffer, J. (ed.) Proceedings of the 3rd International Conference on Genetic Algorithms, pp. 2–9. Morgan Kaufmann, San Francisco (1989)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Institute of Computer Sciences and TelecommunicationSiberian State Aerospace UniversityKrasnoyarskRussia

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