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)
Abstract
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
For citation: Panteleev A.V., Rakitianskii V.M. Development of a Modified Self-Organizing Migration Optimization Algorithm (MSOMA). Modelirovanie i analiz dannikh = Modelling and Data Analysis, 2020. Vol. 10, no. 2, pp. 62–73. DOI: 10.17759/mda.2020100205. (In Russ., аbstr. in Engl.)
References
- 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.
- 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.
- Davendra D., Zelinka I. Self-Organizing Migrating Algorithm. Methodology and Implementation // Studies in Computational Intelligence, Vol. 626. Springer. 2016. V. 626.
- Пантелеев А.В., Скавинская Д.В. Метаэвристические алгоритмы глобальной оптимизации. – М.: Вузовская книга, 2019.
- Пантелеев А.В. Метаэвристические алгоритмы оптимизации законов управления динамическими системами. – М.: Факториал, 2020.
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