T-MOEA/D: MOEA/D with objective transform in multi-objective problems

Hai Lin Liu*, Fang Qing Gu, Yiu Ming CHEUNG

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

39 Citations (Scopus)

Abstract

To approximate the Pareto optimal solutions of a multi-objective optimization problem, Zhang and Li [8] have recently developed a novel multi-objective evolutionary algorithm based on decomposition (MOEA/D). It can work well if the curve shape of the Pareto-optimal front is friendly. Otherwise, it might fail. In this paper, we propose an improved MOEA/D algorithm (denoted as TMOEA/D), which utilizes a monotonic increasing function to transform each individual objective function into the one so that the curve shape of the non-dominant solutions of the transformed multi-objective problem is close to the hyper-plane whose intercept of coordinate axes is equal to one in the original objective function space. Consequently, we can approximate the Pareto optimal solutions that are uniformly distributed over the Pareto front using the advanced decomposition technique of MOEA/D. Numerical results show that the proposed algorithm has a good performance.

Original languageEnglish
Title of host publicationProceedings - 2010 International Conference of Information Science and Management Engineering, ISME 2010
Pages282-285
Number of pages4
DOIs
Publication statusPublished - 2010
Event2010 International Conference of Information Science and Management Engineering, ISME 2010 - Xi'an, China
Duration: 7 Aug 20108 Aug 2010

Publication series

NameProceedings - 2010 International Conference of Information Science and Management Engineering, ISME 2010
Volume2

Conference

Conference2010 International Conference of Information Science and Management Engineering, ISME 2010
Country/TerritoryChina
CityXi'an
Period7/08/108/08/10

Scopus Subject Areas

  • Information Systems and Management

User-Defined Keywords

  • Evolutionary algorithm
  • Multi-objective optimization
  • Pareto front
  • Uniformly distribution

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