Abstract: The Non-dominated Sorting Genetic Algorithm with elite strategy（NSGA-II） based on NSGA is one of the classical algorithms to solve multi-objective optimization problems. Congestion and congestion comparison operator are proposed by NSGA-II, which replace the fitness sharing strategy which needs to specify the shared radius. However, the exclusion mechanism based on crowding distance in NSGA-II to maintain population diversity has a defect in the front of the pareto frontier. Therefore, an improved algorithm named SN-NSGA2 which considers individual neighborhood is proposed. The idea of neighborhood in the density clustering algorithm DBSCAN is applied to new exclusion mechanism, and simultaneously a method of constructing individual neighborhood with corresponding elimination strategy is put forward. Experimental results show that the new algorithm has better distribution and good convergence.

Key words: Non-dominated Sorting Genetic Algorithm with elite strategy（NSGA2）, multiobjective optimization, neighborhood, diversity, crowding distance

摘要： 带有精英策略的非支配排序遗传算法（NSGA-II）是在NSGA的基础之上，提出拥挤度和拥挤度比较算子，代替了需要指定共享半径的适应度共享策略，是解决多目标优化问题的经典算法之一。但是NSGA-II算法在保持种群多样性时采取的拥挤距离排挤机制有着pareto前沿分布不均匀的缺陷，因此，提出一种基于个体邻域的改进NSGA-II算法SN-NSGA2。SN-NSGA2将密度聚类算法DBSCAN中邻域的思想应用到排挤机制中去，提出一种个体邻域的构建方法，采用相应的淘汰策略去除个体邻域中的其他邻居个体。实验结果表明相对于NSGA-II算法来说，新算法求出的pareto解集有着更好的分布性以及良好的收敛性。

关键词: 带有精英策略的非支配排序遗传算法（NSGA2）, 多目标优化, 邻域, 分布性, 拥挤距离