Integration of preferences in decomposition multiobjective optimization

Ke LI, Renzhi CHEN, Geyong MIN, Xin YAO

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

57 Citations (Scopus)

Abstract

Rather than a whole Pareto-optimal front, which demands too many points (especially in a high-dimensional space), the decision maker (DM) may only be interested in a partial region, called the region of interest (ROI). In this case, solutions outside this region can be noisy to the decision-making procedure. Even worse, there is no guarantee that we can find the preferred solutions when tackling problems with complicated properties or many objectives. In this paper, we develop a systematic way to incorporate the DM's preference information into the decomposition-based evolutionary multiobjective optimization methods. Generally speaking, our basic idea is a nonuniform mapping scheme by which the originally evenly distributed reference points on a canonical simplex can be mapped to new positions close to the aspiration-level vector supplied by the DM. By this means, we are able to steer the search process toward the ROI either directly or interactively and also handle many objectives. Meanwhile, solutions lying on the boundary can be approximated as well given the DM's requirements. Furthermore, the extent of the ROI is intuitively understandable and controllable in a closed form. Extensive experiments on a variety of benchmark problems with 2 to 10 objectives, fully demonstrate the effectiveness of our proposed method for approximating the preferred solutions in the ROI. © 2018 IEEE.
Original languageEnglish
Article number8440670
Pages (from-to)3359-3370
Number of pages12
JournalIEEE Transactions on Cybernetics
Volume48
Issue number12
Early online date20 Aug 2018
DOIs
Publication statusPublished - Dec 2018
Externally publishedYes

Bibliographical note

This work was supported in part by the Royal Society under Grant IEC/NSFC/170243, in part by the Ministry of Science and Technology of China under Grant 2017YFC0804003, in part by the Science and Technology Innovation Committee Foundation of Shenzhen under Grant ZDSYS201703031748284), in part by the Shenzhen Peacock Plan under Grant KQTD2016112514355531, and in part by EPSRC under Grant EP/J017515/1 and Grant EP/P005578/1. This paper was recommended by Associate Editor H. Li.

Keywords

  • Decomposition-based method
  • evolutionary multiobjective optimization (EMO)
  • reference points
  • user-preference incorporation

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