The standard grey wolf optimization (GWO) algorithm has a few disadvantages in the high-dimensional function optimization, including low solution precision, slow convergence speed and high possibility of being trapped in local optimum. A hybrid grey wolf optimization (HGWO) algorithm combined chaotic mapping and elite opposition- based learning strategy was proposed for solving unconstrained high-dimensional function optimization problems. In proposed HGWO algorithm, chaotic sequence was used to initiate individuals’ position, which strengthened the diversity of global searching. Elite opposition-based learning strategy was applied to the current elite individuals, which coordinated the exploration and exploitation ability of the proposed HGWO algorithm. It then disturbed the first three best individuals by chaotic mapping in the process of the search so as to avoid the possibility of falling into local optimum. Numerical experiments were conducted on the 10 high-dimensional (100, 500, and 1000 dimension) classical test functions. The simulation results demonstrate that the proposed HGWO algorithm has better performance in solution precision and convergence speed.