A mixed strategy of combining evolutionary algorithms with multigrid methods

Authors:
Jun He;Lishan Kang
Affiliations:
School of Computer Science, University of Birmingham, Birmingham, UK,School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China;School of Computer Science, China University of Geosciences, Wuhan, China
Venue:
International Journal of Computer Mathematics
Year:
2009

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Abstract

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.