Preference-based multiobjective optimization using truncated expected hypervolume improvement

Kaifeng Yang, Longmei Li, Andre Deutz, Thomas Back, Michael Emmerich

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

18 Citations (Scopus)

Abstract

The ultimate goal of multi-objective optimization is to provide potential solutions to a decision maker. Usually, what they are concerned with is a Pareto front in an interesting/preferred region, instead of the whole Pareto front. In this paper, a method for effectively approximating a preferred Pareto front, based on multiobjective efficient global optimization (EGO), is introduced. EGO uses Gaussian processes (or Kriging) to build a model of the objective function. Our variant of EGO uses truncated expected hypervolume improvement (TEHVI) as an infill criterion, which takes predictive mean, variance and preference region in the objective space into consideration. Compared to expected hypervolume improvement (EHVI), the probability density function in TEHVI follows a truncated normal distribution. This paper proposes a TEHVI method that makes it possible to set a region of interest on the Pareto front and focus search effectively on this preferred region. An expression for the exact and efficient computation of the TEHVI for truncation over a two dimensional range is derived, and benchmark results on standard bi-objective problems for small budget of evaluations are computed, confirming the effectiveness of the new approach.

Original languageEnglish
Title of host publication2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016
EditorsJiayi Du, Chubo Liu, Kenli Li, Lipo Wang, Zhao Tong, Maozhen Li, Ning Xiong
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages276-281
Number of pages6
ISBN (Electronic)9781509040933
DOIs
Publication statusPublished - 19 Oct 2016
Event12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016 - Changsha, China
Duration: 13 Aug 201615 Aug 2016

Publication series

Name2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016

Conference

Conference12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016
Country/TerritoryChina
CityChangsha
Period13.08.201615.08.2016

Keywords

  • Expected Hypervolume Improvement
  • Preferred Region
  • Surrogated-assisted Optimization
  • Truncated Expected Hypervolume Improvement

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