Archive Update Strategy Influences Differential Evolution Performance : научное издание | Научно-инновационный портал СФУ

Archive Update Strategy Influences Differential Evolution Performance : научное издание

Тип публикации: статья из журнала

Год издания: 2020

Идентификатор DOI: 10.1007/978-3-030-53956-6_35

Ключевые слова: Archive set, CEC benchmark, differential evolution, mutation, optimization

Аннотация: In this paper the effects of archive set update strategies on differential evolution algorithm performance are studied. The archive set is generated from inferior solutions, removed from the main population, as the search process proceeds. Next, the archived solutions participate in the search during mutation step, allowing better exploration properties to be achieved. The LSHADE-RSP algorithm is taken as baseline, and 4 new update rules are proposed, including replacing the worst solution, the first found worse solution, the tournament-selected solution and individually stored solution for every solution in the population. The experiments are performed on CEC 2020 single objective optimization benchmark functions. The results are compared using statistical tests. The comparison shows that changing the update strategy significantly improves the performance of LSHADE-RSP on high-dimensional problems. The deeper analysis of the reasons of efficiency improvement reveals that new archive update strategies lead to more successful usage of the archive set. The proposed algorithms and obtained results open new possibilities of archive usage in differential evolution.

Ссылки на полный текст

Издание

Журнал: Lecture Notes in Computer Science (см. в книгах)

Выпуск журнала: Т.12145 LNCS

Номера страниц: 397-404

ISSN журнала: 03029743

Издатель: Springer-Verlag GmbH

Авторы

  • Stanovov V. (Reshetnev Siberian State University of Science and Technology)
  • Akhmedova S. (Reshetnev Siberian State University of Science and Technology)
  • Semenkin E. (Reshetnev Siberian State University of Science and Technology)

Вхождение в базы данных

Информация о публикациях загружается с сайта службы поддержки публикационной активности СФУ. Сообщите, если заметили неточности.

Вы можете отметить интересные фрагменты текста, которые будут доступны по уникальной ссылке в адресной строке браузера.