An Experimental Study of Preference Model Integration into Multi-Objective Optimization Heuristics

The usage of preference models in algorithms for multi-objective optimization has recently received an increasing attention by the research community. Motivated by this trend, we experimentally study the impact that the integration of preference models into evolutionary multi-objective search algorithms has on performance. In this article, we consider three preference models, ranging from rather simple to more complex ones; these are (i) reference point, (ii) guided dominance, and (iii) PROMETHEE II. As a benchmark problem we consider multi-objective traveling salesman problem instances of various sizes and with a varying number of objectives.