Abstract: In order to avoid the situation of falling into local optimum in solving the multi-objective optimization problem(MOP) with differential evolution algorithm(DE) , we designed a bidirectional search mechanism which can improve the ability of local search of the DE. We also designed a multi-population mechanism for DE, which can reduce the risk of local optimum, and make the Pareto fronts more evenly distributed. Experimental results shows that, compared with similar algorithms such as NSUA-II, the proposed method is more efficient, while the precision and distribution of Pareto optimal solution set is better than the former.

Key words: Differential evolution,Multi-o场ective optimization, Multi-population