A novel preference articulation operator for the Evolutionary Multi-Objective Optimisation of classifiers in concealed weapons detection

ROSTAMI, Shahin, O'REILLY, Dean, SHENFIELD, Alex and BOWRING, Nicholas (2015). A novel preference articulation operator for the Evolutionary Multi-Objective Optimisation of classifiers in concealed weapons detection. Information Sciences, 295, 494-520.

Preview

PDF (Preprint of final accepted paper) INS-D-13-2148R2_preprint.pdf - Accepted Version Download (996kB) | Preview

Abstract

The incorporation of decision maker preferences is often neglected in the Evolutionary Multi-Objective Optimisation (EMO) literature. The majority of the research in the field and the development of EMO algorithms is primarily focussed on converging to a Pareto optimal approximation close to or along the true Pareto front of synthetic test problems. However, when EMO is applied to real-world optimisation problems there is often a decision maker who is only interested in a portion of the Pareto front (the Region of Interest) which is defined by their expressed preferences for the problem objectives. In this paper a novel preference articulation operator for EMO algorithms is introduced (named the Weighted Z-score Preference Articulation Operator) with the flexibility of being incorporated a priori, a posteriori or progressively, and as either a primary or auxiliary fitness operator. The Weighted Z-score Preference Articulation Operator is incorporated into an implementation of the Multi-Objective Evolutionary Algorithm Based on Decomposition (named WZ-MOEA/D) and benchmarked against MOEA/D-DRA on a number of bi-objective and five-objective test problems with test cases containing preference information. After promising results are obtained when comparing WZ-MOEA/D to MOEA/D-DRA in the presence of decision maker preferences, WZ-MOEA/D is successfully applied to a real-world optimisation problem to optimise a classifier for concealed weapon detection, producing better results than previously published classifier implementations.

Item Type:	Article
Research Institute, Centre or Group - Does NOT include content added after October 2018:	Cultural Communication and Computing Research Institute > Communication and Computing Research Centre
Identification Number:	https://doi.org/10.1016/j.ins.2014.10.031
Page Range:	494-520
Depositing User:	Alex Shenfield
Date Deposited:	27 Nov 2014 14:33
Last Modified:	18 Mar 2021 04:47
URI:	https://shura.shu.ac.uk/id/eprint/8771

Actions (login required)

View Item

Downloads

Publication Metrics