Abstract: To solve the shortcomings of sparrow search algorithm,such as slow convergence speed,easy to fall into local optimal value and low optimization precision,an improved sparrow search algorithm(IM-SSA) based on Various improvement strategies is proposed.Firstly,the initial population of the sparrow search algorithm is enriched by Tent chaotic sequence,which expand the search area.Then,the adaptive crossover and mutation operator is introduced into the finders to enrich the diversity of the producers population and balance the global and local search ability of the algorithm.Secondly,the t-distribution perturbation or differential mutation is used to perturbate the population after each iteration according to the individual characteristics,which can avoid the population singularity in the later stage of the algorithm and enhance the ability of jump out of the local optimal value of the algorithm.Finally,the IM-SSA algorithm proposed in this paper,gray Wolf algorithm,particle swarm optimization algorithm,whale algorithm and classical sparrow search algorithm are used to simulate the eight test functions,respectively.Through the comparative analysis of simulation results,it can be concluded that the IM-SSA algorithm has faster convergence speed,stronger ability to get out of local optimal value and higher optimization precision than the other four algorithms.Compared the simulation results of the IM-SSA algorithm with the ones of the existing improved sparrow search algorithm,it is found that the strategy of IM-SSA algorithm proposed in this paper is better.

Key words: Crossover mutation operator, Differential evolution algorithm, Sparrow search algorithm, t-distribution perturbation