A Simple but Efficient Ranking-Based Differential Evolution

DOI Web Site 被引用文献5件 参考文献18件 オープンアクセス
  • TODO Yuki
    Faculty of Electrical, Information and Communication Engineering, Kanazawa University
  • LI Jiayi
    Faculty of Engineering, University of Toyama
  • GAO Shangce
    Faculty of Engineering, University of Toyama
  • YI Junyan
    Beijing University of Civil Engineering and Architecture
  • YANG Haichuan
    Faculty of Engineering, University of Toyama
  • YANG Lin
    Faculty of Engineering, University of Toyama

書誌事項

公開日
2022-01-01
資源種別
journal article
DOI
  • 10.1587/transinf.2021edl8053
公開者
一般社団法人 電子情報通信学会

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説明

<p>Differential Evolution (DE) algorithm is simple and effective. Since DE has been proposed, it has been widely used to solve various complex optimization problems. To further exploit the advantages of DE, we propose a new variant of DE, termed as ranking-based differential evolution (RDE), by performing ranking on the population. Progressively better individuals in the population are used for mutation operation, thus improving the algorithm's exploitation and exploration capability. Experimental results on a number of benchmark optimization functions show that RDE significantly outperforms the original DE and performs competitively in comparison with other two state-of-the-art DE variants.</p>

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