Skip to Main Content

Modelling and Data Analysis
2020. Vol. 10, no. 2, 62–73
doi:10.17759/mda.2020100205
ISSN: 2219-3758 / 2311-9454 (online)

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

 
Audio is AI-generated
365

,

PDF (RUS)
Online translation in english
GS
Information
in Google Scholar
Metrics

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.

General Information

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

Journal rubric: Optimization Methods

Article type: scientific article

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

Published

For citation: Panteleev, A.V., Rakitianskii, V.M. (2020). Development of a Modified Self-Organizing Migration Optimization Algorithm (MSOMA). Modelling and Data Analysis, 10(2), 62–73. (In Russ.). https://doi.org/10.17759/mda.2020100205

© Panteleev A.V., Rakitianskii V.M., 2020

License: CC BY-NC 4.0

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.

Information About the Authors

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

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

Metrics

 Web Views

Whole time: 551
Previous month: 20
Current month: 4

 PDF Downloads

Whole time: 365
Previous month: 6
Current month: 0

 Total

Whole time: 916
Previous month: 26
Current month: 4