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Using the method of artificial immune systems to seek the suboptimal program control of deterministic systems

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Abstract

Determination of the suboptimal programmed control for the continuous deterministic systems by the use of the method of artificial immune systems for search of the conditional global extremum was proposed. An algorithm to solve it which underlies the corresponding software was generated, and a modification of the method was suggested to enhance its efficiency. Examples of solutions of the model problems were given, and the method of artificial immune systems was compared with its modification, as well as with other metaheuristic methods.

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References

  1. de Castro, L.N. and von Zuben, F.J., Learning and Optimization Using the Clonal Selection Principle, IEEE Trans. Evol. Comput., 2002, vol. 6, no. 3, pp. 239–251.

    Article  Google Scholar 

  2. de Castro, L. and Timmis, J., An Artificial Immune Network for Multimodal Function Optimization, in Proc. IEEE Congr. Evol. Comput., 2002, vol. 1, pp. 669–674.

    Google Scholar 

  3. de Castro, L.N., von Zuben, F.J., and Knidel, H., Artificial Immune Systems, in Proc. 7 Int. Conf. ICARIS, Heidelberg, 2007, vol. 4628.

  4. Dasgupta, D., Artificial Immune Systems and Their Applications, Berlin: Springer, 1999. Translated under the title Iskusstvennye immunye sistemy i ikh primenenie, Moscow: Fizmatlit, 2006.

    Book  MATH  Google Scholar 

  5. Metlitskaya, D.V., Algorithmic Support of the Modified Method of Artificial Immune Systems, in Teoreticheskie voprosy vychislitel’noi tekhniki i programmnogo obespecheniya. Mezhvuz. sb. nauchn. tr. MIREA (Theoretical Issues of Computer Engineering and Software. MIREA Collected Papers), 2011, pp. 81–86.

    Google Scholar 

  6. Panteleev, A.V. and Bortakovskii, A.S., Teoriya upravleniya v primerakh i zadachakh (Control Theory in Examples and Problems), Moscow: Vysshaya Shkola, 2003.

    Google Scholar 

  7. Panteleev, A.V., Metaevristicheskie algoritmy poiska global’nogo extremuma (Metaheuristic Algorithms to Seek Global Extremum), Moscow: MAI-PRINT, 2009.

    Google Scholar 

  8. Kireev, V.I. and Panteleev, A.V., Chislennye metody v primerakh i zadachakh (Numerical Methods in Examples and Problems), Moscow: Vysshaya Shkola, 2008.

    Google Scholar 

  9. Panteleev, A.V. and Metlitskaya, D.V., Formation of the Genetic Algorithms with Real Coding in the Problem of Design of Optimal Control of D Systems, Aviakosm. Priborostr., 2011, no. 3, pp. 26–31.

    Google Scholar 

  10. Panteleev, A.V. and Metlitskaya, D.V., Application of the Genetic Algorithms with Real Coding to the Problem of Optimal Control of Discrete Systems, Vestn. Comput. Inf. Technol., 2011, no. 9, pp. 17–23.

    Google Scholar 

  11. Panteleev, A.V. and Metlitskaya, D.V., An Application of Genetic Algorithms with Binary and Real Coding for Approximate Synthesis of Suboptimal Control in Deterministic Systems, Autom. Remote Control, 2011, vol. 72, no. 11, pp. 2328–2338.

    Article  MATH  MathSciNet  Google Scholar 

  12. Panteleev, A.V. and Metlitskaya, D.V., Application of the Genetic Algorithms with Binary Coding to the Problem of Seeking the Optimal Control of Continuous Deterministic Systems, Aviakosm. Priborostr., 2011, no. 2, pp. 23–30.

    Google Scholar 

  13. Zubanov, N.V., Analiz ustoichivosti otnositel’no postavlennoi tseli kak odin iz podkhodov k opisaniyu funktsionirovaniya organizatsii v usloviyakh neopredelennosti (Analysis of Stability to the Posed Purpose as an Approach to Describing Operation of an Organization under Uncertainty), Samara: Samar. Gos. Tekhn. Univ., 2001.

    Google Scholar 

  14. Farhad Nadi, A Parameter-Less Genetic Algorithm with Customized Crossover and Mutation Operators, in Proc. 13 Conf. GECCO, Dublin, 2011, pp. 901–908.

    Google Scholar 

  15. Lopez Cruz, I.L., van Willigenburg, L.G., and van Straten, G., Evolutionary Algorithms for Optimal Control of Chemical Processes, in Wageningen Univ. Res. Publicat., Netherlands, 2000, pp. 155–161.

    Google Scholar 

  16. Luus, R., Iterative Dynamic Programming, New York: Chapman&Hall/CRC, 2000.

    Book  MATH  Google Scholar 

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Correspondence to A. V. Panteleev.

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Original Russian Text © A.V. Panteleev, D.V. Metlitskaya, 2014, published in Avtomatika i Telemekhanika, 2014, No. 11, pp. 38–54.

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Panteleev, A.V., Metlitskaya, D.V. Using the method of artificial immune systems to seek the suboptimal program control of deterministic systems. Autom Remote Control 75, 1922–1935 (2014). https://doi.org/10.1134/S0005117914110034

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  • DOI: https://doi.org/10.1134/S0005117914110034

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