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 INSPM’s task is to find a non-uniform sampling of the Pareto-...

Full description

Saved in:
Bibliographic Details
Published in:Information sciences Vol. 268; pp. 202 - 219
Main Authors: Pedro, Luciana R., Takahashi, Ricardo H.C.
Format: Journal Article
Language:English
Published: Elsevier Inc 01.06.2014
Subjects:
Evolutionary computation
Interactive algorithm
Multi-objective optimization
Progressive preference
RBF network
Utility function model
Utility function model
Progressive preference
RBF network
Multi-objective optimization
Interactive algorithm
Evolutionary computation
ISSN:0020-0255, 1872-6291
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 INSPM’s task is to find a non-uniform sampling of the Pareto-optimal front with a detailed sampling of the DM’s 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 DM’s utility function using ordinal information only, extracted from queries to the DM. The INSPM algorithm calls the DM’s 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.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2013.12.045