Objective reduction based on nonlinear correlation information entropy

Handing Wang, Xin Yao

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)
105 Downloads (Pure)

Abstract

It is hard to obtain the entire solution set of a many-objective optimization problem (MaOP) by multi-objective evolutionary algorithms (MOEAs) because of the difficulties brought by the large number of objectives. However, the redundancy of objectives exists in some problems with correlated objectives (linearly or nonlinearly). Objective reduction can be used to decrease the difficulties of some MaOPs. In this paper, we propose a novel objective reduction approach based on nonlinear correlation information entropy (NCIE). It uses the NCIE matrix to measure the linear and nonlinear correlation between objectives and a simple method to select the most conflicting objectives during the execution of MOEAs. We embed our approach into both Pareto-based and indicator-based MOEAs to analyze the impact of our reduction method on the performance of these algorithms. The results show that our approach significantly improves the performance of Pareto-based MOEAs on both reducible and irreducible MaOPs, but does not much help the performance of indicator-based MOEAs.

Original languageEnglish
Pages (from-to)2393–2407
JournalSoft Computing
Volume20
Issue number6
Early online date17 Mar 2015
DOIs
Publication statusPublished - Jun 2016

Keywords

  • Dimension reduction
  • Multi-objective evolutionary algorithm
  • Multi-objective optimization
  • Nonlinear correlation information entropy
  • Objective reduction

ASJC Scopus subject areas

  • Software
  • Geometry and Topology
  • Theoretical Computer Science

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