Ra-dominance: A new dominance relationship for preference-based evolutionary multiobjective optimization

Abstract

While traditional Pareto-based evolutionary multi-objective optimization (EMO) algorithms have shown an excellent balance between convergence and diversity on a wide range of practical problems with two or three objectives in real applications, the decision maker (DM) is interested in a unique set of solutions rather than the whole population on Pareto optimal front (POF). In addition, Pareto-based EMO algorithms have some shortcomings in dealing with many-objective problems because of insufficient selection pressure toward trade-off solutions. Due to the above, it is crucial to incorporate DM preference information into EMO and seek a representative subset of Pareto optimal solutions with an increase in the number of objectives. This paper proposes a new dominance relationship, called Ra-dominance, which can improve diversity among the Pareto-equivalent solutions increase the selection pressure in evolutionary process. It has the ability to guide the population toward areas more responsive to the needs of the DM according to a reference point and preference angle. We use the new dominance relationship in the NSGA-II algorithm, and the efficacy and usefulness of the modified procedure are assessed through two- to ten-objective problems. Experimental results show that the algorithm applying this new dominance relationship is highly competitive when compared with four state-of-the-art preference-based EMO methods.