Scalarizing Functions in Decomposition-Based Multiobjective Evolutionary Algorithms

Shouyong Jiang, Shengxiang Yang* (Corresponding Author), Yong Wang, Xiaobin Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

68 Citations (Scopus)


Decomposition-based multiobjective evolutionary algorithms (MOEAs) have received increasing research interests due to their high performance for solving multiobjective optimization problems. However, scalarizing functions (SFs), which play a crucial role in balancing diversity and convergence in these kinds of algorithms, have not been fully investigated. This paper is mainly devoted to presenting two new SFs and analyzing their effect in decomposition-based MOEAs. Additionally, we come up with an efficient framework for decomposition-based MOEAs based on the proposed SFs and some new strategies. Extensive experimental studies have demonstrated the effectiveness of the proposed SFs and algorithm.

Original languageEnglish
Pages (from-to)296-313
Number of pages18
JournalIEEE Transactions on Evolutionary Computation
Issue number2
Early online date29 Jul 2017
Publication statusPublished - Apr 2018

Bibliographical note

This work was supported in part by
the Engineering and Physical Sciences Research Council of U.K. under Grant
EP/K001310/1, in part by the National Natural Science Foundation of China
under Grant 61673331 and Grant 61673397, and in part by the EU Horizon
2020 Marie Sklodowska-Curie Individual Fellowships under Project 661327.

Publisher Copyright:
© 1997-2012 IEEE.

Data Availability Statement

This paper has supplementary downloadable multimedia material available
at provided by the authors.
Color versions of one or more of the figures in this paper are available
online at


  • Decomposition
  • evolutionary algorithm
  • multiobjective optimization
  • scalarizing function (SF)


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