Adaptive component crossover for differential evolution in solving single-objective optimization problems : доклад, тезисы доклада

Sopov Anton; Sopov Evgenii

Дополнительные ссылки: eLibrary · Источник статьи

База данных: Каталог библиотеки СФУ

Библиографическое описание: Adaptive component crossover for differential evolution in solving single-objective optimization problems : доклад, тезисы доклада / A. A. Sopov, E. E. Sopov. - [S. l. : s. n.], 2025. - Текст : непосредственный // ITM Web of Conferences : III International Workshop / Hybrid Methods of Modeling and Optimization in Complex Systems (HMMOCS-III 2024) (2024 ; 02.12 - 04.12 ; Krasnoyarsk). - Krasnoyarsk, 2025. - P. 5001. - 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/20257205001.

Аннотация: With the increase of the complexity of engineering problems, evolutionary algorithms became an effective approach to black-box optimization problems. One of the most popular and promising evolutionary methods is the Differential Evolution algorithms. This method involves several evolutionary operators, including crossover, which is used to form offspring based on mutant and parent vectors, and is important in forming new generations of solutions. However, the classic differential evolution and its numerous modifications usually tends to use the single crossover mechanism to each of the variables of the system, therefore the properties and role of the subcomponents are not considered. That may lead to a slower convergence and increasing demands on computing resources. In this study we have proposed a novel Adaptive Component Crossover strategy for differential evolution, in which the crossover rate parameter is represented by a vector and its values are based on the behavior of the objective function on separate components. The experimental results on a set of benchmark problems have shown that the proposed scheme can improve the performance of the algorithm and, in particular, increase the convergence speed and crossover success rate.

Год издания: 2025

Авторы: Sopov Anton , Sopov Evgenii

Издательство: Krasnoyarsk

Источник: ITM Web of Conferences

Номера страниц: 5001

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-III 2024) (Krasnoyarsk, 2024 ; 02.12 - 04.12)

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