Empirical Studies on the Role of the Decision Maker in Interactive Evolutionary Multi-Objective Optimization

The interactive evolutionary multi-objective optimization (IEMO) algorithms aim to learn and utilize the preference information from the decision maker (DM) during the optimization process to guide the search towards preferred solutions. In this paper, we are devoted to figuring out the effects of interaction patterns, DM calls, preference changes, and DM inconsistencies on the quality of the solutions generated by the IEMO algorithms. The investigation is done in the context of I-MOEA/D-PLVF algorithm, a recently proposed interactive optimization algorithm based on MOEA/D. The experimental results indicate that different interaction patterns and the number of DM calls do result in significant impacts on the quality of the obtained solutions generated by the IEMO algorithm used in our experiments. Meanwhile, preference changes and DM inconsistencies in the process of interactions will impose irreversibly negative effects on obtained solutions.