INSPM: An interactive evolutionary multi-objective algorithm with preference model
In this paper an interactive method for modeling the preferences of a Decision-Maker (DM) is employed to guide a modified version of the NSGA-II algorithm: the Interactive Non-dominated Sorting algorithm with Preference Model (INSPM). The INSPMs task is to find a non-uniform sampling of the Pareto-optimal front with a detailed sampling of the DMs preferred regions and a coarse sampling of the non-preferred regions. In the proposed technique, a Radial Basis Function (RBF) network is employed to construct a function which represents the DMs utility function using ordinal information only, extracted from queries to the DM. The INSPM algorithm calls the DMs preference model via a Dynamic Crowding Distance (DCD) density control method which provides the mechanism for increasing the sampling in the preferred regions and for decreasing it in non-preferred regions which allows a fine-tunning control of the Pareto-optimal front sampling density.
multi-objective optimization evolutionary computation utility function model progressive preference interactive algorithm rbf network