基于聚类的NSGA-II算法

采用精英策略的非支配排序遗传算法（NSGA-II）种群收敛分布不均匀，全局搜索能力较弱。针对该问题，基于现有的算法，提出一种基于聚类学习机制的多目标进化算法KMCNSGA—II。利用K均值聚类对目标函数和个体分别进行聚类，对聚类后的个体进行局部学习，以提高适应度。将该算法应用于经典的多目标约束和非约束测试函数中，通过收敛性指标世代距离和多样性指标△进行性能评价。实验结果表明，与NSGA—II算法相比，该算法在算法收敛性和种群多样性保持方面均有明显提高。

Non-dominated Sorting Genetic Algorithm II Based on Clustering

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.