计算机工程Issue(12)：186-190,5.DOI:10.3969/j.issn.1000-3428.2013.12.040
基于聚类的NSGA-II算法
Non-dominated Sorting Genetic Algorithm II Based on Clustering
摘要
Abstract
According to the uneven distribution of population convergence and poor performance in global search of Non-dominated Sorting Genetic Algorithm II(NSGA-II), a multi-objective evolutionary algorithm, called K-means clustering non-dominated sorting genetic algorithm II(KMCNSGAII) is proposed with combining the theory and the existing algorithm. The KMCNSGAII uses K-means clustering technology and at the same time clusters both all the objective functions and individuals respectively. Then the learning and improvement method is used with respect to individuals after clustering. The KMCNSGAII algorithm is applied to several classical unconstrained and constrained test functions. Experimental results demonstrate that the KMCNSGAII achieves good results with performance evaluation about convergence indicator and diversity indicator, in convergence and diversity of population both are improved significantly compared with NSGA-II.关键词
多目标进化算法/多目标优化/K均值聚类/非支配排序遗传算法II/局部搜索/Pareto前沿Key words
Multi-objective Evolutionary Algorithm(MOEA)/multi-objective optimization/K-means clustering/Non-dominated Sorting Genetic Algorithm II(NSGA-II)/local search/Pareto front分类
信息技术与安全科学引用本文复制引用
李志强,蔺想红..基于聚类的NSGA-II算法[J].计算机工程,2013,(12):186-190,5.基金项目
国家自然科学基金资助项目(61165002)；甘肃省自然科学基金资助项目(1010RJZA019) （61165002）
