Adjust weight vectors in MOEA/D for bi-objective optimization problems with discontinuous Pareto fronts

Chunjiang Zhang, Kay Chen Tan, Loo Hay Lee, Liang Gao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

32 Citations (Scopus)

Abstract

Multi-objective evolutionary algorithm based on decomposition (MOEA/D) is a recently proposed algorithm which is a research focus in the field of multi-objective evolutionary optimization. It decomposes a multi-objective problem into subproblems by mathematic programming methods and applies evolutionary algorithms to optimize the subproblems simultaneously. MOEA/D is good at finding Pareto solutions which are evenly distributed. However, it can be improved for problems with discontinuous Pareto fronts (PF). Many solutions will assemble in breakpoints in this situation. A method for adjusting weight vectors for bi-objective optimization problems with discontinuous PF is proposed. Firstly, this method detects the weight vectors which need to be adjusted using a property of MOEA/D. Secondly, the reserved vectors are divided into several subsets. Thirdly, after calculating the ideal number of vectors in each subset, vectors are adjusted evenly. Lastly, the corresponding solutions are updated by a linear interpolation. Numerical experiment shows the proposed method obtains good diversity and convergence on approached PF.

Original languageEnglish
Pages (from-to)3997-4012
Number of pages16
JournalSoft Computing
Volume22
Issue number12
DOIs
Publication statusPublished - 1 Jun 2018
Externally publishedYes

Keywords

  • Adjust weight vector
  • Discontinuous Pareto fronts
  • MOEA/D
  • Multi-objective evolutionary algorithm (MOEA)

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Geometry and Topology

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