中文摘要：

基于目标向量的多偏好协同进化算法无法识别处于同一适应值水平上的候选解之间的Pareto支配关系，导致所获解集在Pareto前沿分布不均匀．鉴于此种情况，文中提出基于混合支配策略的多偏好协同进化算法．首先对种群进行Pareto支配排序，再计算候选解的适应值，降低种群中非支配解比例，增加选择压力．同时，将目标空间中候选解的距离信息融入到适应值赋值方法中，惩罚处于同一适应值水平但距离理想解较远的候选解，提高解集前沿的分布均匀性．最后在12个WFG系列和DTLZ系列测试函数上的实验表明，文中算法在大部分测试函数上所获解集整体质量较优．

英文摘要：

The preference-inspired co-evolutionary algorithm employing goal vectors can not identify the Pareto dominance relationship of candidate solutions at the same fitness level, and the obtained solutions are unevenly distributed along the Pareto front. Aiming at these problems, preference-inspired co-evolutionary algorithm based on hybrid domination strategy （E-PICEA-g） is proposed in this paper. Firstly, Pareto dominance sorting on population is conducted, and then the candidate solutions fitnessvalues are calculated to reduce the proportion of non-dominated solutions in the population and increase the selection pressure. Meanwhile, the distance between candidate solutions and ideal point is considered to punish the candidate solutions at the same fitness level but far from the ideal point. Thus, the obtained solutions are made to distribute evenly along the Pareto optimal front. Experimental results on 12 multi-objective optimization functions demonstrate that the proposed algorithm acquires solutions with high quality on most of the test functions.