Choice of Spacecraft Control Contour Variant with Self-configuring Stochastic Algorithms of Multi-criteria Optimization

Maria Semenkina, Shakhnaz Akhmedova, Christina Brester, Eugene Semenkin

2016

Abstract

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. Corresponding optimization multi-objective problems with algorithmically given functions of mixed variables are solved with a special stochastic algorithms called Self-configuring Non-dominated Sorting Genetic Algorithm II, Cooperative Multi-Objective Genetic Algorithm with Parallel Implementation and Co-Operation of Biology Related Algorithms for solving multi-objective integer optimization problems which require no settings determination and parameter tuning. The high performance of the suggested algorithms is proved by solving real problems of the control contours structure preliminary design.

References

  1. Akhmedova, Sh., Semenkin, E., 2013. Co-Operation of Biology Related Algorithms. In Proc. of Congress on Evolutionary Computation (CEC 2013), pp. 2207- 2214.
  2. Akhmedova, Sh., Semenkin, E., 2015. Co-Operation of Biology-Related Algorithms for Multiobjective Constrained Optimization. In ICSI-CCI 2015, Part I, LNCS 9140, pp. 487-494.
  3. Abraham, A., Jain, L., Goldberg, R., 2005. Evolutionary multiobjective optimization: theoretical advances and applications. New York: Springer Science, 302 p.
  4. Brester, C., Semenkin, E., 2015. Cooperative MultiObjective Genetic Algorithm with Parallel Implementation. In Proc. of ICSI-2015, pp. 471-478.
  5. Corne, D., Knowles, J., Oates, M., 2000. The Pareto envelope-based selection algorithm for multiobjective optimization. In PPSN VI, Parallel Problem Solving from Nature. Springer, pp. 839-848.
  6. Corne, D., Jerram, N., Knowles, J., Oates, M., 2001. PESAII: Region-based selection in evolutionary multiobjective optimization. In GECCO 2001, Proceedings of the Genetic and Evolutionary Computation Conference, pp. 283-290.
  7. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T., 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. In IEEE Transactions on Evolutionary Computation 6 (2), pp. 182-197.
  8. Horn, J., Nafpliotis, N., Goldberg, D., 1994. A niched Pareto genetic algorithm for multiobjective optimization. In CEC-1994, pp. 82-87.
  9. Kennedy, J., Eberhart, R., 1995. Particle Swarm Optimization. In Proc. of IEEE International Conference on Neural networks, IV, pp. 1942-1948.
  10. Semenkin, E., Semenkina, M., 2012. Spacecrafts' Control Systems Effective Variants Choice with SelfConfiguring Genetic Algorithm. In Proc. of the 9th International Conference on Informatics in Control, Automation and Robotics, vol. 1, pp. 84-93.
  11. Wang, R., 2013. Preference-Inspired Co-evolutionary Algorithms. A thesis submitted in partial fulfillment for the degree of the Doctor of Philosophy, University of Sheffield, 231 p.
  12. Yang, Ch., Tu, X., Chen, J., 2007. Algorithm of Marriage in Honey Bees Optimization Based on the Wolf Pack Search. In Proc. of the International Conference on Intelligent Pervasive Computing, pp. 462-467.
  13. Zhang, Q., Zhou, A., Zhao, S., Suganthan, P. N., Liu, W., Tiwari, S., 2008. Multi-objective optimization test instances for the CEC 2009 special session and competition. University of Essex and Nanyang Technological University, Tech. Rep. CES-487.
  14. Zitzler, E., Laumanns, M., Thiele, L., 2002. SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multiobjective Optimization. In Evolutionary Methods for Design Optimization and Control with Application to Industrial Problems, 3242 (103), pp. 95- 100.
Download


Paper Citation


in Harvard Style

Semenkina M., Akhmedova S., Brester C. and Semenkin E. (2016). Choice of Spacecraft Control Contour Variant with Self-configuring Stochastic Algorithms of Multi-criteria Optimization . In Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-198-4, pages 281-286. DOI: 10.5220/0006009502810286


in Bibtex Style

@conference{icinco16,
author={Maria Semenkina and Shakhnaz Akhmedova and Christina Brester and Eugene Semenkin},
title={Choice of Spacecraft Control Contour Variant with Self-configuring Stochastic Algorithms of Multi-criteria Optimization},
booktitle={Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2016},
pages={281-286},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006009502810286},
isbn={978-989-758-198-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Choice of Spacecraft Control Contour Variant with Self-configuring Stochastic Algorithms of Multi-criteria Optimization
SN - 978-989-758-198-4
AU - Semenkina M.
AU - Akhmedova S.
AU - Brester C.
AU - Semenkin E.
PY - 2016
SP - 281
EP - 286
DO - 10.5220/0006009502810286