Does Preference Always Help? A Holistic Study on Preference-Based Evolutionary Multiobjective Optimization Using Reference Points

Ke LI, Minhui LIAO, Kalyanmoy DEB, Geyong MIN, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

42 Citations (Scopus)

Abstract

The ultimate goal of multiobjective optimization is to help a decision maker (DM) identify solution(s) of interest (SOI) achieving satisfactory tradeoffs among multiple conflicting criteria. This can be realized by leveraging DM's preference information in evolutionary multiobjective optimization (EMO). No consensus has been reached on the effectiveness brought by incorporating preference in EMO (either a priori or interactively) versus a posteriori decision making after a complete run of an EMO algorithm. Bearing this consideration in mind, this article: 1) provides a pragmatic overview of the existing developments of preference-based EMO (PBEMO) and 2) conducts a series of experiments to investigate the effectiveness brought by preference incorporation in EMO for approximating various SOI. In particular, the DM's preference information is elicited as a reference point, which represents her/his aspirations for different objectives. The experimental results demonstrate that preference incorporation in EMO does not always lead to a desirable approximation of SOI if the DM's preference information is not well utilized, nor does the DM elicit invalid preference information, which is not uncommon when encountering a black-box system. To a certain extent, this issue can be remedied through an interactive preference elicitation. Last but not the least, we find that a PBEMO algorithm is able to be generalized to approximate the whole PF given an appropriate setup of preference information. © 1997-2012 IEEE.
Original languageEnglish
Article number9066927
Pages (from-to)1078-1096
Number of pages19
JournalIEEE Transactions on Evolutionary Computation
Volume24
Issue number6
Early online date14 Apr 2020
DOIs
Publication statusPublished - Dec 2020
Externally publishedYes

Keywords

  • Decision-making
  • evolutionary multiobjective optimization (EMO)
  • preference incorporation
  • reference point

Fingerprint

Dive into the research topics of 'Does Preference Always Help? A Holistic Study on Preference-Based Evolutionary Multiobjective Optimization Using Reference Points'. Together they form a unique fingerprint.

Cite this