A Multi-Surrogate Assisted Salp Swarm Feature Selection Algorithm with Multi-Population Adaptive Generation Strategy for Classification

Abstract

The salp swarm algorithm(SSA) has been successfully used to solve the feature selection problem due to its fast convergence and simple structure. However, existing SSA-based methods still suffer from the issue of low classification accuracy due to the problem of getting trapped in local optima. Therefore, this paper proposes a novel feature selection method for classification based on SSA, which can continuously generate new sub-populations to improve the search environment of the main population. Specifically, a flip-prohibition(F-P) operator is first proposed to help the main population, which may currently fall into a local optimum, find a new and more promising region. A multi-surrogate technique is suggested to evaluate the region to determine the position of sub-populations, which can reduce the high computational cost. In addition, a population initialization method is developed according to the importance of features and the dimensionality of the dataset. Finally, a communication mechanism is presented to enable different sub-populations to learn from each other. By comparing the proposed method with other 6 feature selection methods on 16 datasets, we demonstrate that the proposed method has better classification ability and can select a smaller feature subset in most cases.