Обзор программных средств для работы с эволюционными и роевыми методами оптимизации
Аннотация
Настоящая статья посвящена обзору программных средств, позволяющих применять, разрабатывать и исследовать эволюционные и роевые методы оптимизации для решения сложных задач дискретной и непрерывной оптимизации. В статье рассматриваются различные виды и типы оптимизационных задач, возникающих в прикладных задачах, включая задачи многокритериальной оптимизации. Формализуется понятие популяционного алгоритма оптимизации и рассматриваются основные классы алгоритмов данного типа, в том числе эволюционные алгоритмы, роевые алгоритмы и многочастичные алгоритмы. Приводятся результаты детального анализа тринадцати современных наиболее популярных фреймворков для работы с эволюционными и роевыми алгоритмами оптимизации. Основной целью анализа является исследование предоставляемых данными программными средствами возможностей по созданию, настройке и применению популяционных алгоритмов оптимизации для решения прикладных оптимизационных задач. В том числе анализируются типы поддерживаемых рассматриваемыми программными средствами задач оптимизации, а также наличие встроенных средств тестирования и наборов тестовых задач оптимизации. Отдельный пункт исследования посвящен анализу поддержки рассматриваемыми фреймворками параллельных вычислений, так как известно, что применение популяционных алгоритмов с одной стороны является вычислительно затратным, а с другой стороны такие алгоритмы обладают значительным потенциалом к распараллеливанию. На основании результатов проведенного обзора даются рекомендации по применению рассмотренных программных средств в различных сценариях их практического использования.
Литература
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33. Rashedi E., Nezamabadi-Pour H., Saryazdi S. GSA: a gravitational search algorithm. Information sciences. 2009;179(13):2232-2248. https://doi.org/10.1016/j.ins.2009.03.004
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38. Blank J., Deb K. Pymoo: Multi-objective optimization in python. IEEE Access. 2020;8:89497-89509. https://doi.org/10.1109/ACCESS.2020.2990567
39. Durillo J.J., Nebro A.J. jMetal: A Java framework for multi-objective optimization. Advances in engineering software. 2011;42(10):760-771. https://doi.org/10.1016/j.advengsoft.2011.05.014
40. Coletti M.A., Scott E.O., Bassett J.K. Library for evolutionary algorithms in Python (LEAP). In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (GECCO '20). New York, NY, USA: Association for Computing Machinery; 2020. p. 1571-1579. https://doi.org/10.1145/3377929.3398147
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Опубликована
2025-04-28
Как цитировать
NIKOLASHKIN, Aleksei Gerasimovich; ERSHOV, Nikolay Mikhailovich.
Обзор программных средств для работы с эволюционными и роевыми методами оптимизации.
Современные информационные технологии и ИТ-образование, [S.l.], v. 21, n. 1, p. 113-126, apr. 2025.
ISSN 2411-1473. Доступно на: <http://sitito.cs.msu.ru/index.php/SITITO/article/view/1120>. Дата доступа: 24 aug. 2025
doi: https://doi.org/10.25559/SITITO.021.202501.113-126.
Раздел
Научное программное обеспечение в образовании и науке
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