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.
Multi-objective Evolutionary Algorithm(MOEA),
Non-dominated Sorting Genetic Algorithm II(NSGA-II),