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基于个体邻域的改进NSGA-Ⅱ算法 被引量:12

Improved NSGA-Ⅱ Algorithm Based on Individual Neighborhood
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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。 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.
作者 董骏峰 王祥 梁昌勇 DONG Junfeng;WANG Xiang;LIANG Changyong(School of Management,Hefei University of Technology,Hefei 230009,China)
出处 《计算机工程与应用》 CSCD 北大核心 2019年第5期166-174,共9页 Computer Engineering and Applications
基金 国家重点研发计划(No.2016YFC0803203) 国家自然科学基金重点项目(No.71331002) 国家自然科学基金面上项目(No.71771075) 国家自然科学基金青年科学基金(No.71301037)
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 Non-dominated Sorting Genetic Algorithm with elite strategy(NSGA2) multiobjective optimization neigh-borhood diversity crowding distance
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