Evolutionary many-objective algorithm using decomposition-based dominance relationship

Lei Chen, Hai Lin Liu, Kay Chen Tan, Yiu Ming Cheung, Yuping Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

19 Citations (Scopus)


Decomposition-based evolutionary algorithms have shown great potential in many-objective optimization. However, the lack of theoretical studies on decomposition methods has hindered their further development and application. In this paper, we first theoretically prove that weight sum, Tchebycheff, and penalty boundary intersection decomposition methods are essentially interconnected. Inspired by this, we further show that highly customized dominance relationship can be derived from decomposition for any given decomposition vector. A new evolutionary algorithm is then proposed by applying the customized dominance relationship with adaptive strategy to each subpopulation of multiobjective to multiobjective framework. Experiments are conducted to compare the proposed algorithm with five state-of-the-art decomposition-based evolutionary algorithms on a set of well-known scaled many-objective test problems with 5 to 15 objectives. Simulation results have shown that the proposed algorithm can make better use of the decomposition vectors to achieve better performance. Further investigations on unscaled many-objective test problems verify the robust and generality of the proposed algorithm.

Original languageEnglish
Article number8457246
Pages (from-to)4129-4139
Number of pages11
JournalIEEE Transactions on Cybernetics
Issue number2
Publication statusPublished - Dec 2019
Externally publishedYes


  • Dominance relationship
  • Evolutionary algorithm
  • Many-objective
  • Multiobjective to multiobjective (M2M) decomposition

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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