Abstract

In order to balance the exploration and exploitation of differential evolution(DE) algorithm, a novel mutation strategy, named "elite/rand/1", is been proposed in the paper, which divide population into two sub-population on the basis of the fitness and extract maximum information from the elite individual. The corresponding DE variant with "elite/rand/1" mutation strategy is tested on a set of 13 benchmark functions. Numerical and statistical results indicate the competence of the proposed DE for solving unconstrained global optimization problems.

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