Generals cheme of complex systems models of self-organization algorithms construction using evolutionary search

V. F. Irodov, R. V Barsuk


.  Purpose. There are occur tasks in the mathematical modeling, the object of study which are complex systems. Convection, heat transfer and other are the objects examples of study. In addition, such systems could be difficult for study. Because of that, number of small experimental data often obtained. Therefore, the main question is to find mathematical model constructing variant with small amount of input data. The self-organization of mathematical models designed by A.G. Ivahnenko is chose.

Methodology. In comparison with other methods, such as analysis of variance, this method is relatively new. However, during its short stories, it has practical value already proved. The clarification of generating models gives by structural algorithm of self-organization that illustrated at Pic.1. “Partial description” has a special importance. It characterizes the complexity of the model and possibility of its using. Linear “private description” is using most commonly as it pointed in the article. However, considering the scope of the using this method, linear description cannot describe the object of study. Therefore, it proposed to use non-linear “partial description” for this method. In addition, there are examples of non-linear functions that could using during description of the object.

Findings. There are methods of evolutionary search for self-organization of mathematical models are developed. This is search reviewed more detailed in the article and shown a block-diagram of the algorithm at Pic.2. It is suggest using “partial description” in a nonlinear form. There are description to this algorithm and shown the importance of “partial description” form for building object of study a mathematical model.

Originality. There is developed construction of complex systems mathematical models general scheme by using self-organization with model parameters “partial description” evolutionary search. There is distinctive convergence with probability 1 process model parameters “partial description” evolutionary search.

Practical value. Models self-organization general scheme using evolutionary solutions search has been realized programmatically. It is indicated the possibility of these methods more widely using and increasing the simulation accuracy in comparison with existing methods


inductive method of self-organization models of complex systems; evolutionary search; algorithm; similarity criterion; partial description


Bukatova L.L., Mikhasov Y.I., Sharov A.M. Evoinformatika: Teoriya i praktika evolutsionnogo modelirovaniya [Evoinformatics : Theory and practice of evolutionary modeling]. Moscow, Nauka Publ., 1991. 203 p.

Ivahnenko A.G., Zaychenko Yu.P., Dimitrov V.D. Prinyatie resheniy na osnove samoorganizatsii [Adoption of decision based on self-organization]. Moscow, Sovetskoe radio Publ., 1976. 277 p.

Ivakhnenko A.G. Inductivnyy metod samoorganizatsii modeley slozhnikh sistem [The inductive method of selforganization models of complex systems]. Kiev, Naukova dumka Publ., 1982. 293 p.

Irodov V.F. O postroenii i skhodimosti algoritmov samoorganizatsii sluchaynogo poiska [The construction and convergence of random search algorithms for selforganization]. Avtomatyka – Automation, 1987, issue 4, pp. 34-43.

Kutateladze S.S. Spravochnik po teploperedache [Handbook of heat transfer]. Novosibirsk, Nauka Publ., 1970. 658 p.

Irodov V.F. Self-organization methods for analysis of nonlinear systems with binary choice relations / System analysis modeling simulation, 1995, vol. 18-19, pp. 203-206.

Farlow S. J. Self-organizing methods in modeling. Statistics : Textbook and monographs, 1984, vol. 54, pp. 45.

Frank L. Self-organization modeling for decision support. International conference in inductive modeling ICIM’2013. [Knowledge Miner Software]. Berlin, 172-178 p.

Godfrey C.O. Design of hybrid differential evolution and group method of data handling for inductive modeling.Information science, 2011, vol. 178, no. 18, pp. 87.

Mueller J. A., Lemke F. Self-organization data mining. An intelligent approach to extract knowledge from data. Berlin, 1999. 225 p.

Madala H.R, Ivakhnenko A.G. Inductive learning algorithms for complex systems modelling. London, CRC Press Inc. Boca Raton. Ann Arbor Publ., 1994. 250 p.

Wasniowski R.A. Using self-organization networks for intrusion detection. Proceedings of the 6th WSEAS int. conf. of Neural Networks. Lisbon, 2005, pp. 90-94.

Russel I. Neural networks module. Hartford, 2012. 115 p.

Turki Y.A., Abdulkareem A.Y., Lamya D.A. Financial prediction using inductive models. Basrah Journal of science, 2013, vol. 31 (2), pp. 64-72

Yasmeen M.Z. System identification using group method of data handling. Sharjah, 2009. 87 p.

Vikas L., Sanita L. An adapted group method of data handling for abrupt data analysis. International journal of engineering and computer science ISSN:2319-7242, 2014, vol. 3, no. 2, pp. 3842-3851.

GOST Style Citations

1.  Букатова,  Л. Л.  Эвоинформатика  :  Теория  и практика  эволюционного  моделирования  /  Л. Л.  Букатова, Ю. И.  Михасов,  А. М.  Шаров.  –  Москва  :  Наука,  1991.  – 203 с.

2.  Ивахненко,  А.  Г.  Принятие  решений  на  основе самоорганизации  /  А.  Г.  Ивахненко,  Ю.  П.  Зайченко, В. Д. Димитров. – Москва : Советское радио, 1976. – 277 с.

3.  Ивахненко,  А. Г.  Индуктивный  метод самоорганизации  моделей  сложных  систем / А. Г. Ивахненко. – Киев: Наукова думка, 1982. – 293 с.

4.  Иродов,  В. Ф.  О  построении  и  сходимости алгоритмов  самоорганизации  случайного  поиска / В. Ф. Иродов // Автоматика. – 1987. – № 4. – С. 34-43.

5.  Кутателадзе,  C.  С.  Справочник  по  теплопередаче / C. С.  Кутателадзе.  –  4-е  изд.,  дополн.  –  Новосибирск  : Наука, 1970. – 658 с.

6.  Irodov,  V. F.  Self-organization  methods  for  analysis of nonlinear systems with binary choice relations / V. F. Irodov // System analysis modeling simulation.  – 1995. – Vol. 18-19. –pp. 203-206.

7.  Farlow,  S.  J.  Self-organizing  methods  in  modeling / S. J. Farlow // Statistics : Textbook and monographs.  –  1984. – Vol. 54. – pp. 45.

8.  Frank,  L.  Self-organization  modeling  for  decision support  /  L.  Frank  //  International  conference  in  inductive modeling  ICIM’2013  /  Knowledge  miner  software.  –  Berlin, 2013. – pp. 172-178.

9.  Godfrey, C. O. Design of hybrid differential evolution and  group  method  of  data  handling  for  inductive  modeling / C. O. Godfrey // Information science. – Fiji, 2011. – Vol. 178, issue 18. – pp. 87.

10.  Mueller,  J. A.  Self-organization  data  mining.  An intelligent  approach  to  extract  knowledge  from  data / J. A. Mueller, F. Lemke. – Berlin, 1999. – 225 p.

11.  Madala,  H. R.  Inductive  learning  algorithms  for complex systems modelling / H. R. Madala, A. G. Ivakhnenko. – London : CRC Press Inc. Boca Raton, 1994. – 250 p.

12.  Wasniowski, R. A. Using self-organization networks for intrusion detection / A. R. Wasniowski // Proceedings of the 6th WSEAS  int.  conf.  of  Neural  Networks.  –  Lisbon,  2005.  – pp. 90-94.

13.  Russel,  I.  Neural  networks  module  /  I.  Russel.  – Hartford, 2012. – 115 p.

14.  Turki,  Y. A.  Financial  prediction  using  inductive models  /  A.  Y.  Turki,  A.  Y.  Abdulkareem,  D.  A.  Lamya // Basrah Journal of science. – 2013. – Vol. 31 (2). – pp. 64-72.

15.  Yasmeen,  M.  Z.  System  identification  using  group method of data handling / M. Z. Yasmeen.  –  Sharjah, 2009.  – 87 p.

16.  Vikas, L. An adapted group method of data handling for  abrupt  data  analysis  /  L.  Vikas,  L.  Sanita  //  International journal of engineering and computer science ISSN : 2319-7242. – Jaipur, 2014. – Vol. 3, № 2. – pp. 3842-3851.


  • There are currently no refbacks.