Abstract: Aiming at the shortcomings of Salp Swarm Algorithm (SSA: Salp Swarm Algorithm) that are easy to fall into local optimality and slow convergence when solving feature selection problems, Based on salp swarm optimization algorithm, its improved version, differential evolution salp swarm feature selection algorithm(DESSA: Differential Evolution Salp Swarm Algorithm) is proposed. Differential evolution strategy is applied to replace the ordinary operator as the new way of moving particles to enhance search capabilities. And evolutionary population dynamics ( EPD: Evolution Population Dynamics) is proposed to enhance convergence efficiency.Utilizing K-nearest neighbor (KNN: K-Nearest Neighbor) as classifier and eight datasets come from the UCI (University of California Irvine) machine learning repository, DESSA is compared with the SSA and other high performing approaches proposed recently. The experimental result confirms the efficiency of DESSA in improving the SSA in several respects and its ability to better solve the problem of feature selection compared with other approaches of feature selection.

Key words: feature selection, salp swarm optimization algorithm, differential evolution, evolutionary population dynamics (EPD)