Abstract: Aiming at solving  problems of  high parameter dimension, complex calculation and the fitness depended on  other tools with GA, we proposed an aggregation operator to accelerate the convergence of genetic algorithm. Firstly, the affinity propagation (AP) clustering was used to divide the population into sub-clusters, and then the dimension of each cluster was reduced by principal component analysis (PCA). Secondly, the population distribution was fitted to quadric surfaces by weighted least square method in lower dimensional space. Finally, the calculated extreme points were returned to the original space as the dominant individuals. The experimental results show that compared with the traditional genetic algorithm, aggregation operator can effectively improve the convergence speed and ensure the optimization accuracy.

Key words: complex problems solving, genetic algorithm, fast convergence, aggregation operator