(Non) convergence results for the differential evolution method

Marco Locatelli, Massimiliano Vasile

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

In this paper we deal with the convergence properties of the differential evolution (DE) algorithm, a rather popular stochastic method for solving global optimization problems. We are going to show there exist instances for which the basic version of DE has a positive probability not to converge (stagnation might occur), or converges to a single point which is not a local minimizer of the objective function, even when the objective function is convex. Next, some minimal corrections of the basic DE scheme are suggested in order to recover convergence with probability one to a local minimizer at least in the case of strictly convex functions.

Original languageEnglish
Pages (from-to)413-425
Number of pages13
JournalOptimization Letters
Volume9
Issue number3
Early online date18 Oct 2014
DOIs
Publication statusPublished - 1 Mar 2015

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Keywords

  • convergence
  • differential evolution
  • stagnation
  • DE
  • differential evolution algorithms

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