Dynamic multi-objective differential evolution based on improved strategies

In order to improve the ability of differential evolution (DE) to solve dynamic multi-objective optimization problems (DMOPs), in this paper, the information of historical population is used to designed a improved mutation operator, this operation to make up the deficiencies of the diversity for population. In addition, when the environment is changed, use center points and linear prediction model to design a prediction technique, this technique is used to track the changed optimal solution set. Merge the above strategies into NSGA-II and noted as (HPS-NSGA-II/DE). Finally, the performance of HPS-NSGA-II/DE is confirmed by some test functions.