CWDE: A Novel LSHADE Variant with Cauchy Distribution-based Weight Selection

Kaiyu Wang*, Sicheng Liu, Lingyu Qi*, Jiaru Yang, Shangce Gao*

*この論文の責任著者

研究成果: 書籍の章/レポート/会議録会議への寄与査読

抄録

Differential evolution (DE) is a long-standing methodology for resolving intricate optimization concerns. LSHADE is an effective variation of DE. It has been successful in numerous applications and is highly regarded in the field. Our paper introduces a novel variant of LSHADE, namely CWDE, which replaces the conventional greedy selection in DE with a weight selection method grounded on Cauchy distribution (CW). To evaluate CWDE's performance, we test it on the 2017 IEEE Congress on Evolutionary Computation (CEC) benchmark functions. The experimental results confirm that CWDE outperforms LSHADE and other advanced competitors.

本文言語英語
ホスト出版物のタイトルIEEE ITAIC 2023 - IEEE 11th Joint International Information Technology and Artificial Intelligence Conference
編集者Bing Xu, Kefen Mou
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1296-1300
ページ数5
ISBN(電子版)9798350333664
DOI
出版ステータス出版済み - 2023
イベント11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023 - Chongqing, 中国
継続期間: 2023/12/082023/12/10

出版物シリーズ

名前IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)
ISSN(印刷版)2693-2865

学会

学会11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023
国/地域中国
CityChongqing
Period2023/12/082023/12/10

ASJC Scopus 主題領域

  • 人工知能
  • 情報システム

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