Differential evolution based on improved learning strategy

Abstract

From a learning perspective, the mutation scheme in differential evolution (DE) can be regarded as a learning strategy. When mutating, three random individuals are selected and placed in a random order. This strategy, however, probably suffers some drawbacks which can slow down the convergence rate. To improve the efficiency of classic DE, this paper proposes a differential evolution based on improved learning strategy (ILSDE). The proposed learning strategy, inspired by the learning theory of Confucius, places the three individuals in a more reasonable order. Experimenting with 23 test functions, we demonstrate that ILSDE performs better than classic DE. © 2008 Springer Berlin Heidelberg.