Preferences-Based Choice Prediction in Evolutionary Multi-objective Optimization
Evolutionary multi-objective algorithms (EMOAs) of the type of NSGA-2 approximate the Pareto-front, after which a decision-maker (DM) is confounded with the primary task of selecting the best solution amongst all the equally good solutions on the Pareto-front. In this paper, we complement the popular NSGA-2 EMOA by posteriori identifying a DM’s best solution among the candidate solutions on the Pareto-front, generated through NSGA-2. To this end, we employ a preference-based learning approach to learn an abstract ideal reference point of the DM on the multi-objective space, which reflects the compromises the DM makes against a set of conflicting objectives. The solution that is closest to this reference-point is then predicted as the DM’s best solution. The pairwise comparisons of the candidate solutions provides the training information for our learning model. The experimental results on ZDT1 dataset shows that the proposed approach is not only intuitive, but also easy to apply, and robust to inconsistencies in the DM’s preference statements.
KeywordsMulti-objective optimization NSGA-2 Preference-based learning Solution selection
This work was carried out at Computational Intelligence Group, Department of Computing Science, University of Oldenburg, Germany with the support of German Academic Exchange Service (DAAD) to Manish Aggarwal as a visiting scientist and university academician.
- 10.Chaudhari, P., Dharaskar, R., Thakare, V.M.: Computing the most significant solution from pareto front obtained in multi-objective evolutionary. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 1(4), 1–6 (2010)Google Scholar
- 11.Zio, E., Bazzo, R.: A comparison of methods for selecting preferred solutions in multiobjective decision making. In: Kahraman, C. (ed.) Computational Intelligence Systems in Industrial Engineering. Atlantis Computational Intelligence Systems, vol. 6, pp. 23–43. Springer, Heidelberg (2012)CrossRefGoogle Scholar