In this paper, a target region based multiobjective evolutionary algorithm framework is proposed to incorporate preference into the optimization process. It aims at finding a more fine-grained resolution of a target region without exploring the whole set of Pareto optimal solutions. It can guide the search towards the regions on the Pareto Front which are of real interest to the decision maker. The algorithm framework has been combined with SMS-EMOA, R2-EMOA, NSGA-II to form three target region based multiobjective evolutionary algorithms: T- SMS-EMOA, T-R2-EMOA and T-NSGA-II. In these algorithms, three ranking criteria are applied to achieve a well-converged and well-distributed set of Pareto optimal solutions in the target region. The three criteria are: 1. Non-dominated sorting; 2. indicators (hypervolume or R2 indicator) or crowding distance in the new coordinate space (i.e. target region) after coordinate transformation; 3. the Chebyshev distance to the target region. Rectangular and spherical target regions have been tested on some benchmark problems, including continuous problems and discrete problems. Experimental results show that new algorithms can handle the preference information very well and find an adequate set of Pareto-optimal solutions in the preferred regions quickly. Moreover, the proposed algorithms have been enhanced to support multiple target regions and preference information based on a target point or multiple target points. Some results of enhanced algorithms are presented.
