A Cooperative Co-evolutionary Salp Swarm Feature Selection Algorithm with Reverse Escape Strategy for Classification

Zikang Yu, Hongbin Dong, Jing Sun, Bingxu Zhao

Abstract: As one of the swarm intelligence algorithms, Salp Swarm Algorithm (SSA) is widely used in feature selection problems due to its fast convergence speed and simple structure. However, SSA still has the disadvantages of poor population diversity and being prone to fall into local optimum. To solve these problems, this study proposes a cooperative co-evolutionary salp swarm feature selection algorithm (CESSA) for classification. Firstly, different populations are divided according to the characteristics of the salps, which solves the drawback of a single chain in the SSA algorithm. Multiple populations are generated from mutual information (MI)-based, random, chaotic map-based initialization corresponding to different search tasks. Learning the habitats of the solitary salps and modeling them ensures the exploration ability of the algorithm. Finally, a cooperative strategy is presented to integrate the advantages of different populations to achieve cooperative co-evolution. CESSA is evaluated on fourteen datasets and compared with six feature selection methods. The results show that CESSA can select smaller feature subsets with higher classification accuracy in most cases.