A Novel Binary DE Based on the Binary Search Space Topology : доклад, тезисы доклада

Sopov Evgenii

Дополнительные ссылки: eLibrary · Источник статьи

База данных: Каталог библиотеки СФУ

Библиографическое описание: A Novel Binary DE Based on the Binary Search Space Topology : доклад, тезисы доклада / E. E. Sopov. - [S. l. : s. n.], 2022. - Текст : непосредственный // HYBRID METHODS OF MODELING AND OPTIMIZATION IN COMPLEX SYSTEMS : Proceedings of the International Workshop “Hybrid methods of modeling and optimization in complex systems” (HMMOCS 2022) / Krasnoyarsk Regional Science and Technology City Hall, Russia Siberian Federal University, Reshetnev Siberian State University of Science and Technology ; International Workshop “Hybrid methods of modeling and optimization in complex systems” (HMMOCS 2022) (2022 ; 22.11 - 24.11 ; Krasnoyarsk). - London, United Kingdom : European Proceedings, 2022. - P. 328-335. - ISBN 9781802969603, DOI 10.15405/epct.23021.40.

Аннотация: Differential evolution is known as a simple and well-argued evolutionary algorithm that demonstrates the high performance in many hard black-box optimization problems with continuous variables. The main feature of differential evolution is the difference-based mutation. The mutation explores the search space using the distribution of points in the population and usually can well adapt to the objective function landscape. There exist some modifications of differential evolution for the binary search space. The proposed approach involves the understanding of the binary space topology for developing a better analogue of the difference-based mutation. We have compared the proposed binary differential evolution algorithm with the standard binary genetic algorithm using a set of binary test problems, including hard deceptive problems. The preliminary experimental results shows that new binary differential evolution is a competitive search algorithm and outperforms the binary genetic algorithm in reliability for some problems but yields it in the required number of function evaluations. The proposed approach for developing mutation in DE can be expanded to all other mutation schemes for increasing the performance.

Год издания: 2022

Авторы: Sopov Evgenii

Издательство: European Proceedings, London, United Kingdom

Источник: HYBRID METHODS OF MODELING AND OPTIMIZATION IN COMPLEX SYSTEMS

Номера страниц: 328-335

Держатель оригинала документа: Siberian Federal University

Мероприятие: International Workshop “Hybrid methods of modeling and optimization in complex systems” (HMMOCS 2022) (Krasnoyarsk, 2022 ; 22.11 - 24.11)

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A Novel Binary DE Based on the Binary Search Space Topology : доклад, тезисы доклада
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