The preference-based multi-objective evolutionary algorithms are the sort of evolutionary algorithms to assist the decision maker (DM) in finding interesting Pareto optimal solutions.At present, the inappropriate locations of the reference points sometimes seriously impact the convergence performance of the algorithms when the locations of the reference points are used as the preference information during the optimization.Moreover, the size of the preferred region is difficult to control.And the comprehensive performance of the algorithms will degrade in dealing with many-objective problems.To address the above issues, in this paper, the self-adjustable preference-based radius is calculated to build a new preference relation model, and by dividing region of interest (ROI), a preference-based multi-objective evolutionary algorithm based on the division of RoI is proposed.The proposed algorithm is compared with four reference point based multi-objective evolutionary algorithms (g-NSGA-II, r-NSGA-II, angle-based preference algorithm and MOEA/D-PRE).The results show that the proposed algorithm has good convergence and diversity, and at meantime allows the DM control the size of the preferred region.In addition it has a good convergence in addressing the many-objective problems.