Abstract: NSGA-II is widely used in multi-objective evolutionary optimization for its high convergence and time efficiency.However，the population maintenance based on crowding distance in NSGA-II has not worked well in maintaining the diversity of solution sets.This paper proposes an improved strategy to dynamically maintain diversity by setting a self-adaptive threshold value，and the better diversity individuals have more chances to survive.Comparing new algorithm to NSGA-II and ε-MOEA in five test problems，the results show that improved algorithm efficiently promotes the diversity and achieves efficient convergence at the same time.

摘要： NSGA-II以其良好的收敛性和时间效率广泛应用于多目标优化中，然而其基于聚集距离的种群维护策略并不能很好地保持解集的分布性。提出一种改进的分布性保持策略，设置随种群密集程度自适应变化的阈值，动态地维护种群，使得分布性优秀的个体有更大的生存机会。与NSGA-II和ε-MOEA在5个测试函数上进行比较实验，结果表明改进算法在有效提高分布性的同时，拥有良好的收敛性。