According to the characteristics of differential evolution

a multi-objective evolutionary algorithm based on dynamic population multi-strategy differential models and decomposition (MOEA/D-DPMD) is proposed to solve the expensive problems.The algorithm divides the population into three sub-populations and each sub-population is corresponding to a differential evolution strategy.In order to improve the performance of the algorithm

the size of sub-population is adjusted dynamically on the basis of a differential evolution strategy contribution.Each strategy is adopted to participate in coordination during the evolution process.Through the test simulation on the LZ09 benchmarks with complicated Pareto Set (PS)

MOEA/D-DPMD shows a best performance with a neighborhood size of 25.Via the comparative analysis of different schemes of differential strategy

MOEA/D-DPMD also performs well.The experimental results indicate that MOEA/D-DPMD has a better performance in terms of convergence and diversity compared with MOEA/D and NSGA-II

which is an effective way for solving complex multi-objective optimization problems.