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Previous issue (2020. Vol. 10, no. 2)

Modelling and Data Analysis

Publisher: Moscow State University of Psychology and Education

ISSN (printed version): 2219-3758

ISSN (online): 2311-9454

DOI: https://doi.org/10.17759/mda

License: CC BY-NC 4.0

Started in 2011

Published 4 times a year

Free of fees
Open Access Journal

 

Development of a Modified Self-Organizing Migration Optimization Algorithm (MSOMA) 34

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Panteleev A.V.
Doctor of Physics and Matematics, Professor, Head of the Department of Mathematical Cybernetics, Institute of Information Technologies and Applied Mathematics, Moscow Aviation Institute (National Research University), Moscow, Russia
ORCID: https://orcid.org/0000-0003-2493-3617
e-mail: avpanteleev@inbox.ru

Rakitianskii V.M.
Undergraduate Student of the Institute of Information Technology and Applied Mathematics, Moscow Aviation Institute (National Research University), Moscow, Russia
ORCID: https://orcid.org/0000-0001-7894-7462
e-mail: rymbelv@gmail.com

Abstract
The modified self-organizing migration optimization algorithm (MSOMA) based on a self-organizing migration algorithm (SOMA) is suggested. An algorithm for solving the problem of finding the global conditional extremum of the objective function on a given set is developed. Examples illustrating the application of the algorithm and created software are given.

Keywords: global optimization algorithm, migration cycle, population, individual, benchmark problems

Column: Optimization Methods

DOI: https://doi.org/10.17759/mda.2020100205

For Reference

References
  1. Zelinka I., Lampinen J. SOMA–Self-Organizing Migrating Algorithm // Proceedings of the 6th International Conference on Soft Computing (Mendel 2000), Brno, Czech Republic, pp. 177–187.
  2. Zelinka I., Lampinen J., Noulle L. On the theoretical proof of convergence for a class of SOMA search algorithms // Proceedings of 7th International Conference on Soft Computing (Mendel 2001), Brno, Czech Republic, pp. 103–110.
  3. Davendra D., Zelinka I. Self-Organizing Migrating Algorithm. Methodology and Implementation // Studies in Computational Intelligence, Vol. 626. Springer. 2016. V. 626.
  4. Пантелеев А.В., Скавинская Д.В. Метаэвристические алгоритмы глобальной оптимизации. – М.: Вузовская книга, 2019.
  5. Пантелеев А.В. Метаэвристические алгоритмы оптимизации законов управления динамическими системами. – М.: Факториал, 2020.
 
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