Gradient-based optimizer improved by Slime Mould Algorithm for global optimization and feature selection for diverse computation problems

作者：

Highlights：

• Improving the search ability of the GBO optimizer using the SMA algorithm.

• Evaluating the performance of the proposed method using CEC2017 functions.

• Selecting the most relative features to increase the classification accuracy.

摘要

•Improving the search ability of the GBO optimizer using the SMA algorithm.•Evaluating the performance of the proposed method using CEC2017 functions.•Selecting the most relative features to increase the classification accuracy.

论文关键词：Gradient-based optimizer,Slime mould algorithm,Global optimization,Feature selection

论文评审过程：Received 8 February 2022, Revised 4 June 2022, Accepted 17 September 2022, Available online 23 September 2022, Version of Record 30 September 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.118872