Economic Optimization and Evolutionary Programming When Using Remote Sensing Data

  • Roman Shamin
  • Aleksandr SemenovEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1116)


The article considers the issues of optimizing the use of remote sensing data. The following method can be used for the methods of the evaluation of remote sensing approaches. Remote sensing approaches are used in many applications for example for solutions of the complicated problems of transport management and construction of new transport arteries. A mathematical model to describe the economic effect of the use of remote sensing data is built. Here is also given a numerical method of solving this problem. Also discusses how to optimize organizational structure by using genetic algorithm based on remote sensing. The methods considered allow the use of remote sensing data in an optimal way. The proposed mathematical model allows various generalizations for optimization of decision making in the presence of remote sensing data. The approach associated with evolutionary programming is an effective solution when optimizing economic structures in the presence of remote sensing data.


Transportation management system (TMS) Remote sensing Economical optimization Earth monitoring 



The works are done with the financial support of the Ministry of Science and Education of the Russian Federation as part of the research project No. 075-15-2019-249 dated 04.06.2019 (identifier RFMEFI57517X0167).


  1. 1.
    Tikhonov, A.N., Goncharsky, A., Stepanov, V.V., Yagola, A.G.: Numerical Methods for the Solution of Ill-Posed Problems. Springer, Dordrecht (1995)CrossRefGoogle Scholar
  2. 2.
    Cavazzuti, M.: Optimization Methods. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  3. 3.
    Gurevich, P.L., Shamin, R.V., Tikhomirov, S.B.: Reaction-diffusion equations with spatially distributed hysteresis. SIAM J. Math. Anal. 45, 1328 (2013)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Shamin, R.V., Bondarchuk, N.V., Fomina, A.V.: Study of the application of the knowledge formalization and fuzzy logic approaches for processing data obtained from remote sensing. Int. J. Pure Appl. Math. 118(5), 691–693 (2018). Special IssueGoogle Scholar
  5. 5.
    Shamin, R.V., Chursin, A.A., Fedorova, L.A.: The mathematical model of the law on the correlation of unique competencies with the emergence of new consumer markets. Eur. Res. Stud. J. XX(3), Part A, 39 (2017)Google Scholar
  6. 6.
    Groetsch, C.W.: Inverse Problems in the Mathematical Sciences. Vieweg, Braunschweig (1993)CrossRefGoogle Scholar
  7. 7.
    Engl, H.W., Hanke, M., Neubauer, A.: Regularization of Inverse Problems. Kluwer Acad. Publ., Dordrecht (1996)CrossRefGoogle Scholar
  8. 8.
    Shamin, R.V., Yudin, A., Kuznetsov, K., Kurkin, A., Tyugin, D.: Methods and algorithms of freak wave detection in the coastal zone. In: Proceedings of the Twelfth International Conference on the Mediterranean Coastal Environment MEDCOAST, pp. 825–833 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.People’s Friendship University of RussiaMoscowRussia

Personalised recommendations