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This paper proposes an auxiliary framework based on an archive-based covariance matrix and the Gaussian distribution model with shifted mean to improve the gannet optimization algorithm (GOA). A covariance matrix based on an archive is used to build the eigen coordinate system, and samples are taken in it in combination with the shifted mean. To explore the effectiveness of the proposed framework, we compare the results of the framework-assisted GOA on IEEE CEC2017 with other swarm intelligence algorithms, and experimental results demonstrate that framework-assisted GOA is efficient and competitive.

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Enhancing Gannet Optimization Algorithm with Archive-Based Covariance Matrix and Shifted Mean

  • 会议论文

  • 发表日期：

    2025年

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  • Jeng-Shyang Pan,
  • Xing Wang,
  • Shu-Chuan Chu,
  • Wei Li,
  • Chin-Shiuh Shieh

摘要

This paper proposes an auxiliary framework based on an archive-based covariance matrix and the Gaussian distribution model with shifted mean to improve the gannet optimization algorithm (GOA). A covariance matrix based on an archive is used to build the eigen coordinate system, and samples are taken in it in combination with the shifted mean. To explore the effectiveness of the proposed framework, we compare the results of the framework-assisted GOA on IEEE CEC2017 with other swarm intelligence algorithms, and experimental results demonstrate that framework-assisted GOA is efficient and competitive.