Differential evolution algorithm with ensemble of parameters and mutation strategies

Abstract

Differential evolution (DE) has attracted much attention recently as an effective approach for solving numerical optimization problems. However the performance of DE is sensitive to the choice of the mutation strategy and associated control parameters. Thus to obtain optimal performance time-consuming parameter tuning is necessary. Different mutation strategies with different parameter settings can be appropriate during different stages of the evolution. In this paper we propose to employ an ensemble of mutation strategies and control parameters with the DE (EPSDE). In EPSDE a pool of distinct mutation strategies along with a pool of values for each control parameter coexists throughout the evolution process and competes to produce offspring. The performance of EPSDE is evaluated on a set of bound-constrained problems and is compared with conventional DE and several state-of-the-art parameter adaptive DE variants. (C) 2010 Elsevier B.V. All rights reserved.