For the decision making process in real-world applications, multi-objective optimization plays an important role; also, increasing the number of objectives to optimize is so common that this case is specially named as many objective optimization. A main issue with such many objective optimization problems is that, due to space dimension, their solution sets (so-called Pareto sets) can not be computed or entirely approximated. In this paper we present a tool, Pareto Explorer, specifically adapted for a preference-based local exploration of solutions, to deal with linear many objective optimization problems. The Pareto Explorer is able to steer the search from a given solution considering user defined directions, or preferences along the (highly-dimensional) solution set—turning the decision making process more intuitive. We demonstrate the effectiveness of the the proposed method on some benchmark examples.
['Oliver Cuate', 'Adriana Lara', 'Oliver Schütze']
A local exploration tool for linear many objective optimization problems