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

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

 
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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

OpenAlex citations: 5

OpenAlex trends: Advanced Scientific Research Methods, Advanced Data Processing Techniques, Cybersecurity and Information Systems

Information about the work in OpenAlex

Number of citations: 5

Topics

Advanced Scientific Research Methods

This cluster of papers covers a wide range of topics related to innovations in food technology and processing, including mathematical modeling of extraction and production processes, biotechnological aspects of food production, quality assessment, nutritional value, and process optimization. The papers explore various methods and techniques for improving the production, quality, and nutritional value of food products.

Number of works: 26719  |  Total number of citations: 128093

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Advanced Data Processing Techniques

This cluster of papers focuses on the modeling and control of multidimensional systems, with an emphasis on redundant transmission, fuzzy controller adaptation, fault tolerance, real-time systems, and energy efficiency in various domains such as cyber-physical systems, network traffic analysis, and industrial automation.

Number of works: 60256  |  Total number of citations: 206829

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Cybersecurity and Information Systems

This cluster of papers focuses on network security and communication technologies, including IoT networks, telecommunication networks, machine learning, SDN/NFV, cloud services, UAV path planning, big data centers, and information risks.

Number of works: 25080  |  Total number of citations: 34825

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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

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