Cooperative co-evolution with differential grouping for large scale optimization

Mohammad Nabi OMIDVAR, Xiaodong LI, Yi MEI, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

563 Citations (Scopus)

Abstract

Cooperative co-evolution has been introduced into evolutionary algorithms with the aim of solving increasingly complex optimization problems through a divide-and-conquer paradigm. In theory, the idea of co-adapted subcomponents is desirable for solving large-scale optimization problems. However, in practice, without prior knowledge about the problem, it is not clear how the problem should be decomposed. In this paper, we propose an automatic decomposition strategy called differential grouping that can uncover the underlying interaction structure of the decision variables and form subcomponents such that the interdependence between them is kept to a minimum. We show mathematically how such a decomposition strategy can be derived from a definition of partial separability. The empirical studies show that such near-optimal decomposition can greatly improve the solution quality on large-scale global optimization problems. Finally, we show how such an automated decomposition allows for a better approximation of the contribution of various subcomponents, leading to a more efficient assignment of the computational budget to various subcomponents. © 2013 IEEE.
Original languageEnglish
Article number6595612
Pages (from-to)378-393
Number of pages16
JournalIEEE Transactions on Evolutionary Computation
Volume18
Issue number3
Early online date11 Sept 2013
DOIs
Publication statusPublished - Jun 2014
Externally publishedYes

Keywords

  • cooperative co-evolution
  • large-scale optimization
  • non-separability
  • numerical optimization
  • problem decomposition

Fingerprint

Dive into the research topics of 'Cooperative co-evolution with differential grouping for large scale optimization'. Together they form a unique fingerprint.

Cite this