The novel Imperialist Competitive Algorithm (ICA) that was recently introduced has a good performance in some optimization problems. The ICA inspired by socio-political process of imperialistic competition of human being in the real world. In this paper, a new Imperialist Competitive Algorithm with Adaptive Radius of Colonies movement (ICAR) is proposed. In the proposed algorithm, for an effective search, the Absorption Policy changed dynamically to adapt the radius of colonies movement towards imperialist’s position. The ICA is easily stuck into a local optimum when solves high-dimensional multi-modal numerical optimization problems. To overcome this shortcoming, we use probabilistic model that utilize the information of colonies positions to balance the exploration and exploitation abilities of the Imperialist Competitive Algorithm. Using this mechanism, ICA exploration capability will enhance. Some famous unconstraint benchmark functions used to test the ICAR performance. Simulation results show this strategy can improve the performance of the ICA algorithm significantly.