Stable matching-based selection in evolutionary multiobjective optimization

Ke Li, Qingfu Zhang, Sam Kwong, Miqing Li, Ran Wang

Research output: Contribution to journalArticlepeer-review

218 Citations (Scopus)


Multiobjective evolutionary algorithm based on decomposition (MOEA/D) decomposes a multiobjective optimization problem into a set of scalar optimization subproblems and optimizes them in a collaborative manner. Subproblems and solutions are two sets of agents that naturally exist in MOEA/D. The selection of promising solutions for subproblems can be regarded as a matching between subproblems and solutions. Stable matching, proposed in economics, can effectively resolve conflicts of interests among selfish agents in the market. In this paper, we advocate the use of a simple and effective stable matching (STM) model to coordinate the selection process in MOEA/D. In this model, subproblem agents can express their preferences over the solution agents, and vice versa. The stable outcome produced by the STM model matches each subproblem with one single solution, and it tradeoffs convergence and diversity of the evolutionary search. Comprehensive experiments have shown the effectiveness and competitiveness of our MOEA/D algorithm with the STM model. We have also demonstrated that user-preference information can be readily used in our proposed algorithm to find a region that decision makers are interested in.

Original languageEnglish
Article number6678563
Pages (from-to)909-923
Number of pages15
JournalIEEE Transactions on Evolutionary Computation
Issue number6
Early online date5 Dec 2013
Publication statusPublished - 31 Dec 2014


  • decomposition
  • MOEA/D
  • Multiobjective optimization
  • preference incorporation
  • Stable matching deferred acceptance procedure

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Computational Theory and Mathematics


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