  In this paper, a branch and bound algorithm that incorporates the decision
maker's preference information is proposed for multiobjective optimization. In
the proposed algorithm, a new discarding test is designed to check whether a
box contains preferred solutions according to the preference information
expressed by means of reference points. In this way, the proposed algorithm is
able to gradually guide the search towards the region of interest on the Pareto
fronts during the solution process. We prove that the proposed algorithm
obtains $\varepsilon$-efficient solutions distributed in the region of
interest. Moreover, lower bound on the total finite number of required
iterations for predefined precision is also provided. Finally, the algorithm is
illustrated with a number of test problems.
