An improved grey wolf optimization(IGWO) algorithm is proposed to solve global continuous optimization problems. The good point set method is used to initiate the grey wolves individuals' position, which strengthens the diversity of initial individuals in the global searching process. A nonlinear strategy based on the tangent trigonometric function for updating the control parameter is given to balance the exploration and exploitation abilities of the proposed algorithm. Inspired by the particle swarm optimization(PSO) algorithm, a new position update equation of individuals by incorporating the information of individual historical best solution into the position update equation is designed to speed up convergence. The experimental results and comparisons with the classical GWO algorithm and other improved GWO algorithms using a set of well-known benchmark test functions show that the proposed IGWO algorithm can balance the exploration and exploitation to the problem's solution space effectively during evolution.