As the grey wolf algorithm is easy to fall into local optimum and lack of consideration of own experience, this paper proposes a grey wolf optimization algorithm based on particle swarm optimization (PSO_GWO).Firstly, it generates the initial population through Tent chaotic map, which increases the diversity of the population.Then, this paper adopts non-linear control parameters.Its decline speed is slow in the early stage, which can increase the global search ability and prevent the algorithm from falling into the local optimum.The decline speed is quick in the later stage, which can increase the algorithm's local search ability and improve the overall convergence speed.Finally, the idea of particle swarm optimization is introduced to update the position information of individual wolves by combining the best value of the individual with the best value of the population, so as to preserve the best position information of the wolves.In order to verify the effectiveness of the algorithm, this paper compared it with three other algorithms.The experimental results suggested that the solution searched by this paper is more ideal than the other three algorithms on the unimodal function and the multimodal function.The PSO_GWO algorithm worked better than the IGWO algorithm (the improved grey wolf optimization algorithm) in calculating the time complexity; as the population size increased, the convergence value of the PSO_GWO algorithm gradually approached the ideal value.So the proposed algorithm can quickly search the global optimal solution and has better robustness.