中文摘要：

提出一种基于差分进化多目标优化算法．首先，采用基于差分进化的种群启发式搜索，根据多目标优化的特点，进行基于全部种群的Pareto占优比较和选择，有效实现全局搜索和局部搜索．另外，利用一个外部种群来储存非支配解，当非支配解的个数大于外部种群预先设定的规模时，对每个非支配个体采用基于支配关系和拥挤信息的适应度策略评价，然后采用基于密度的选择策略对外部种群进行删减，进一步提高算法的均匀性和宽广性．与NSGA-Ⅱ、PESA-Ⅱ、SPEA2的比较结果表明，该算法不仅收敛性较好，而且在均匀性和宽广性上优势明显．

英文摘要：

A differential evolutionary Multi-objective optimization algorithm （DMOA） is proposed. Firstly, a population heuristic searching mechanism based on differential evolutionary is adopted in the proposed algorithm, moreover, both local search and global search are realized by the whole population based Pareto Dominance comparison and selection according to the characteristic of Multi-objective optimization. In addition, in order to improve the uniformity and spread of algorithm, an external population is used to store the Non-dominated solutions in the proposed algorithm. When the number of the Non-dominated solutions is greater than the predefined size of external population, a fitness strategy based on the dominated relation and crowding information is used to evaluate the Non-dominated solutions, then density-based selection is used to truncate external population. Compared with some typical algorithms such as Non-dominated Sorting Genetic Algorithm （ NSGA- Ⅱ ）, Pareto Envelope-Based Selection Algorithm （PESA-Ⅱ） and Strength Pareto Evolutionary Algorithm （SPEA2）, the proposed algorithm has good convergence and remains a better uniformity and spread.