A mixed strategy of combining evolutionary algorithms with multigrid methods

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Multigrid methods have been proven to be an efficient approach in accelerating the convergence rate of numerical algorithms for solving partial differential equations. This paper investigates whether multigrid methods are helpful to accelerate the convergence rate of evolutionary algorithms for solving global optimization problems. A novel multigrid evolutionary algorithm is proposed and its convergence is proven. The algorithm is tested on a set of 13 well-known benchmark functions. Experiment results demonstrate that multigrid methods can accelerate the convergence rate of evolutionary algorithms and improve their performance.


Original languageEnglish
Pages (from-to)837-849
Number of pages13
JournalInternational Journal of Computer Mathematics
Issue number5
Publication statusPublished - 1 Jan 2009


  • multigrid method, global optimization, evolutionary algorithm, convergence rate, mixed strategy