Hierarchical Preference Algorithm Based On Decomposition Multiobjective Optimization

Rather than a whole Pareto optimal front(POF), which demands too many points, the decision maker (DM) may only be interested in a partial region, called the region of interest(ROI). In this paper, we propose a systematic method to incorporate the DM's preference information into a decomposition-based evolutionary multiobjective optimization algorithm (MOEA/D-HP). Different from most existing decomposition-based preference algorithms, MOEA/D-HP guides the population to converge to the preference region by generating hierarchical reference points in the preference region, and forms some hierarchical solutions for comparison and selection by the DM. In addition, the novel reference vectors generating method of MOEA/D-HP makes the final solutions no longer uniformly distributed in the ROI, instead the closer to the preference point, the denser the obtained solution. Extensive experiments on a variety of benchmark problems with 2 to 15 objectives fully demonstrate the effectiveness of our method in obtaining preferred solutions in the ROI.