With the development of engineering technology and the improvement of mathematical model, a large number of optimization problems have been developed from low dimensional optimization to large-scale complex optimization. Large scale global optimization is an active research topic in the real-parameter optimization. Based on the analysis of the characteristics of large scale problems, a stochastic dynamic cooperative coevolution strategy is proposed in the article. Additionally, a strategy is added to the dynamic multi-swarm particle swarm optimization algorithm. Then, the dual grouping of population and decision variables is realized. Next, the performance of the novel optimization on the set of benchmark functions provided for the CEC2013 special session on large scale optimization is reported. Finally the validity of the algorithm is verified by comparing with other algorithms.