On the performance of standard nature-inspired algorithms in solving non-stationary optimization problems : доклад, тезисы доклада

Cui Kaiwang; Sopov Evgenii

Дополнительные ссылки: eLibrary · Источник статьи

База данных: Каталог библиотеки СФУ

Библиографическое описание: On the performance of standard nature-inspired algorithms in solving non-stationary optimization problems : доклад, тезисы доклада / K. K. Cui, E. E. Sopov. - [S. l. : s. n.], 2024. - Текст : непосредственный // Hybrid Methods of Modeling and Optimization in Complex Systems (HMMOCS-II-2023) : Proceedings of the II International Workshop / Hybrid Methods of Modeling and Optimization in Complex Systems (HMMOCS-II-2023) (2023 ; 28.11 - 30.11 ; Krasnoyarsk). - Krasnoyarsk, 2024. - P. 2023. - This work was supported by the Ministry of Science and Higher Education of the Russian Federation (Grant No.075-15-2022-1121). - DOI 10.1051/itmconf/20245902023.

Аннотация: Non-stationary optimization problems are a very important class of problems in many practical applications. These problems are characterized by objective functions and constraints that change with time or environmental conditions, so the optimization solution also needs to be dynamically adjusted accordingly. Many algorithms from the field of evolutionary and biology inspired computation are known as an effective approach for dealing with hard optimization problems in changing environments, that is the result of modelling of self-organized systems in nature and evolution in the biology. Natural systems always exist in the changing environments. This article aims to compare the performance of three common nature-inspired techniques, namely genetic algorithm (GA), particle swarm optimization (PSO) algorithm, and differential evolution (DE) in their standard implementation when solving non-stationary optimization problems, so as to provide a reference and rationale for subsequent selection of appropriate algorithms and improvements.

Год издания: 2024

Авторы: Cui Kaiwang , Sopov Evgenii

Издательство: Krasnoyarsk

Номера страниц: 2023

Держатель оригинала документа: Reshetnev Siberian State University of Science and Technology, Institute of Informatics and Telecommunications, Siberian Federal University, Institute of Space and Information Technology

Мероприятие: Hybrid Methods of Modeling and Optimization in Complex Systems (HMMOCS-II-2023) (Krasnoyarsk, 2023 ; 28.11 - 30.11)

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On the performance of standard nature-inspired algorithms in solving non-stationary optimization problems : доклад, тезисы доклада
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