Desirable Objective Ranges in Preference-Based Evolutionary Multiobjective Optimization

In this paper, we propose a preference-based Evolutionary Multiobjective Optimization algorithm, at which the preferences are given in the form of desirable ranges for the objective functions, i.e. by means of aspiration and reservation levels. The aspiration levels are values to be achieved by the objectives, while the reservation levels are objective values not to be worsen. In the algorithm proposed, the first generations are performed using a set of weight vectors to initially converge to the region of the Pareto optimal front associated with the point formed with the reservation levels. At a certain moment, these weights are updated using the nondominated solutions generated so far, to redirect the search towards the region which contains the Pareto optimal solutions with objective values among the desirable ranges. To this aim, the remaining number of generations are run using the updated weight vectors and the point formed with the aspiration levels. The computational experiment show the potential of our proposal in 2, 3 and 5-objective problems, in comparison to other state-of-the-art algorithms.