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Using cooperativecoevolution inlarge-scale black-boxconstraintsatisfaction problems: доклад, тезисыдоклада Vakhnin AlekseiNovikov Zakhar2024 год

Using cooperative coevolution in large-scale black-box constraint satisfaction problems : доклад, тезисы доклада
доклад, тезисы доклада, статья из сборника материалов конференций

Дополнительные ссылки: eLibrary · Источник статьи
База данных: Каталог библиотеки СФУ
Библиографическое описание: Using cooperative coevolution in large-scale black-box constraint satisfaction problems : доклад, тезисы доклада / A. A. Vakhnin, Z. Z. Novikov. - [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. 2022. - 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/20245902022.
Аннотация: Solving constrained large-scale global optimization problems poses a challenging task. In these problems with constraints, when the number of variables is measured in the thousands, when the constraints are presented in the form of a black box, and neither the size nor the configuration of the feasible region is known, it is very difficult to find at least one feasible solution. In general, such a problem of finding a feasible region is known as a constraint satisfaction problem. In this paper, we have extended a well-known benchmark set based on constrained optimization problems up to 1000 variables. We have evaluated the CC-SHADE performance, to tackle constraints in large-scale search space. CC-SHADE merges the power of cooperative coevolution and self-adaptive differential evolution. Our extensive experimental evaluations on a range of benchmark problems demonstrate the strong dependence of the performance of CC-SHADE on the number of individuals and the subcomponent number. The numerical results emphasize the importance of using a cooperative coevolution framework for evolutionary-based approaches compared to conventional methods. All numerical experiments are proven by the Wilcoxon test.
Год издания: 2024
Авторы: Vakhnin Aleksei , Novikov Zakhar
Издательство: Krasnoyarsk
Номера страниц: 2022
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)
Количество экземпляров:
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