Dynamic differential evolution algorithm based on elite local learning

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.