Scalarizing Functions in Decomposition-Based Multiobjective Evolutionary Algorithms

Shouyong Jiang, Shengxiang Yang*, Yong Wang, Xiaobin Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

41 Citations (Scopus)

Abstract

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
Volume22
Issue number2
Early online date29 Jul 2017
DOIs
Publication statusPublished - Apr 2018

Keywords

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

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