Research on chaotic flying sparrow search algorithm

Abstract

The sparrow search algorithm has attracted much attention due to its excellent characteristics, but it still has shortcomings such as falling into the local optimum and relying on the initial population stage. In order to improve these shortcomings, the chaotic flying sparrow search algorithm is proposed. In the initialization, the chaotic mapping based on random variables is introduced to make the population distribution more uniform and speed up the optimization efficiency of the population. In the discoverer stage, the dynamic adaptive search strategy and levy flight mechanism are used to increase the search range and flexibility, and the random walk strategy is introduced to make the follower's search more detailed and avoid premature phenomenon. The effectiveness of the improved algorithm is verified by six standard test functions, and the introduction of a variety of strategies greatly enhances the optimization ability of the algorithm.