DE algorithm is simple and efficient

but for complex problems also exist the problem of low efficiency of convergence.In order to improve the global exploration ability and convergence precision

this paper proposes a novel elite local learning dynamic differential evolution algorithm.Firstly the history elites are preserved in the elite pool

and then the elites in the pool conduct local learning by sine functions

finally dynamic DE model is used to effectively improve the speed of convergence

and the convergence of the algorithm is proved in theory.Algorithm has been tested on 20 benchmark functions including unimodal functions and multimodal functions and shift functions

experiments result verified the effectiveness and applicability

and the new algorithm can maintain higher convergence speed while maintaining better convergence accuracy.Comparison with the state-of-the-art DE in statistical analysis proves that the algorithm is a kind of new competitive algorithm.