Genetic improvement of mQSO algorithm for dynamic optimization using PushGP : доклад, тезисы доклада

Stanovov Vladimir; Semenkin Eugene

Дополнительные ссылки: eLibrary · Источник статьи

База данных: Каталог библиотеки СФУ

Библиографическое описание: Genetic improvement of mQSO algorithm for dynamic optimization using PushGP : доклад, тезисы доклада / V. Stanovov, E. Semenkin. - [S. l. : s. n.], 2026. - Текст : непосредственный // Proceedings of the IV International Conference on Advances in Materials, Earth Science and Technology (CAMSTech-IV-2024). - Melville, 2026. - P. 60012. - The reported study was funded by RFBR and FWF according to the research project №21-51-14003. - DOI 10.1063/5.0322422.

Аннотация: This paper describes an approach to improve the multi-population particle swarm optimization algorithm for dynamic problems using genetic programming approach. In particular, the mQSO algorithm is considered, applied to the generalized moving peaks benchmark. The PushGP algorithm is used to generate new local search methods for mQSO instead of the simple random search around the best point. The experiments show that PushGP allows creating new search heuristics automatically, and the best found algorithms improve the mQSO performance.

Год издания: 2026

Авторы: Stanovov Vladimir , Semenkin Eugene

Издательство: Melville

Источник: Proceedings of the IV International Conference on Advances in Materials, Earth Science and Technology (CAMSTech-IV-2024)

Номера страниц: 60012

Держатель оригинала документа: Reshetnev Siberian State University of Science and Technology

Мероприятие: IV International Conference on Advances in Materials, Earth Science and Technology (CAMSTech-IV-2024) (Bukhara, 2024 ; 29.10 - 31.10)

Количество экземпляров:

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