Dispersed foraging slime mould algorithm: Continuous and binary variants for global optimization and wrapper-based feature selection

作者：

Highlights：

• An improved slime mould algorithm (DFSMA) is proposed for feature selection.

• The performance of DFSMA is verified by comparing with several famous algorithms.

• DFSMA has faster convergence speed and accuracy compared with others.

• DFSMA has achieved higher classification accuracy and smaller number of features.

摘要

•An improved slime mould algorithm (DFSMA) is proposed for feature selection.•The performance of DFSMA is verified by comparing with several famous algorithms.•DFSMA has faster convergence speed and accuracy compared with others.•DFSMA has achieved higher classification accuracy and smaller number of features.

论文关键词：Slime mould algorithm,Swarm intelligence,Global optimization,Feature selection

论文评审过程：Received 7 June 2020, Revised 4 September 2021, Accepted 14 November 2021, Available online 27 November 2021, Version of Record 15 December 2021.

论文官网地址：https://doi.org/10.1016/j.knosys.2021.107761