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Memristors enable designing and deploying swarm intelligence algorithms. Here, the firefly algorithm (FA) is binarized and implemented via a memristor crossbar array for feature selection. To address FA’s slow convergence, a novel cosine similarity-based binary firefly algorithm (CS-BFA) is designed, which significantly boosts convergence speed compared to Euclidean distance-based BFA. CS-BFA is implemented using a validated circuit design and control scheme supporting parallel computing. Feature selection for logistic regression (LR) on the Sonar dataset shows that LR with memristive CS-BFA achieves 7.82% higher accuracy using 32 fewer features than competitors.

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Memristive Binary Firefly Algorithm with Cosine Similarity

  • 会议论文

  • 发表日期：

    2025年

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  • Jiarun Shen,
  • Yongbin Yu,
  • Xiangxiang Wang,
  • Xiao Feng,
  • Xuefeng Zhong,
  • Jingya Wang,
  • Xinyi Han,
  • Nyima Tashi

摘要

Memristors enable designing and deploying swarm intelligence algorithms. Here, the firefly algorithm (FA) is binarized and implemented via a memristor crossbar array for feature selection. To address FA’s slow convergence, a novel cosine similarity-based binary firefly algorithm (CS-BFA) is designed, which significantly boosts convergence speed compared to Euclidean distance-based BFA. CS-BFA is implemented using a validated circuit design and control scheme supporting parallel computing. Feature selection for logistic regression (LR) on the Sonar dataset shows that LR with memristive CS-BFA achieves 7.82% higher accuracy using 32 fewer features than competitors.