Archive-based Differential Learning Incorporated Sparrow Search Algorithm

Qianrui Yu, Ziqian Wang, Haotian Li, Yifei Yang, Zhenyu Lei, 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

1 被引用数 (Scopus)

抄録

Sparrow search algorithm (SSA) is a new evolutionary algorithm that has the advantage of good exploration ability. Benefiting from its search strategy, SSA can effectively alleviate the local optima. However, it still suffers from the issue of low solution quality because of its weak exploitation ability. Therefore, we propose an archive-based differential learning incorporated sparrow search algorithm (ASSA), which introduces a local search strategy for enhancing the exploitation ability of SSA. The local search strategy searches for a superior solution in the neighborhood region of the optimal solution in each iteration and uses it to replace the original optimal solution so that SSA can find a better solution. ASSA is compared with four state-of-the-art algorithms on 29 benchmark functions of IEEE CEC2017. The experiment results demonstrate that ASSA has superior performance to its competitors.

本文言語英語
ホスト出版物のタイトルProceedings - 2022 15th International Symposium on Computational Intelligence and Design, ISCID 2022
出版社Institute of Electrical and Electronics Engineers Inc.
ページ212-216
ページ数5
ISBN(電子版)9781665456166
DOI
出版ステータス出版済み - 2022
イベント15th International Symposium on Computational Intelligence and Design, ISCID 2022 - Hangzhou, 中国
継続期間: 2022/12/172022/12/18

出版物シリーズ

名前Proceedings - 2022 15th International Symposium on Computational Intelligence and Design, ISCID 2022

学会

学会15th International Symposium on Computational Intelligence and Design, ISCID 2022
国/地域中国
CityHangzhou
Period2022/12/172022/12/18

ASJC Scopus 主題領域

  • 人工知能
  • コンピュータ グラフィックスおよびコンピュータ支援設計
  • モデリングとシミュレーション

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