协同演化算法在聚类中的应用

提出一种协同演化聚类算法，该算法使用改进的掩码方式动态决定聚类中心的数目。将种群划分成两个子种群，分别采用遗传算法和差分进化算法进行演化，遗传算法侧重于全局寻优，差分进化算法注重于局部搜索。在演化的过程中，利用不同的间隔迁移策略相互交换优良个体，使算法的全局探索能力和局部搜索能力得到均衡。通过性能测试、聚类中心数目和运行时间测试等实验证明该算法的优越性。

A Co-Evolutionary Algorithm for Clustering

A co-evolutionary algorithm for clustering is proposed. Firstly, the number of centers of clusters can be decided automatically with an improved mask code manner. The population is divided into two subpopulations which are constituted of the same size of individuals. The genetic algorithm is used in one subpopulation which is good at global search optimum ability, and the differential evolution algorithm is used in the other which has good local search ability to cluster. In the evolution process, different migration policies are utilized to exchange good individuals found by the two evolutionary algorithms between the twosubpopulations, which can balance the global and local search ability of the proposed algorithm. The experimental results show that the proposed method is effective through testing the number of the centers of clusters, performance and execution time on several datasets.