Earticle

With the continuous improvement of the economic level, people pay more and more attention to the appreciation of art. Art of flower arranging is an art following certain laws of creation and it has a long history. At present, the flower shape depends mainly on the arrangements’ experience and personal preferences. This paper attempts to use genetic algorithm optimized by the K-medoids algorithm for its research, through the replacement of the genetic algorithm to get more design solutions, so as to broaden the designer's ideas, to achieve the innovation of art design. Experiments show that the beauty of floral works produced by the optimized genetic algorithm is better than that produced by traditional genetic algorithm.

Clustering analysis is a descriptive task that seeks to identify homogeneous groups of objects based on the values of their attributes. In this paper we propose a new algorithm for K-medoids clustering which runs like the K-means algorithm. The new algorithm calculates distance matrix once and uses it for finding new medoids at every iterative step. We evaluate the proposed method using real and synthetic data and compare with the results of other algorithms. The proposed algorithm takes reduced time in computation and better performance than others.