一种改进鱼群聚类算法在结构面分组中的应用

An Improved Fish Swarm Clustering Algorithm for Structural Grouping

针对结构面产状常规分类方法存在的不足,提出一种新型的结构面分类算法.基于K-Means算法的结构面分类,将人工鱼群算法(artificial fish swarm algorithm,AFSA)与K-Means算法相结合,建立了AFSA-RSK结构面分类算法.利用鱼群算法强大的寻优能力,代替K-Means算法对结构面产状聚心集进行搜寻,并通过K-Means算法进行聚类.聚类完成后,选择相应参数指标对聚类效果进行评价.针对存在的问题,对鱼群算法的步长和视野进行修正,提高寻找聚心集的精度,动态地调整了聚类过程.将改进后的AFSA-RSK算法与其他算法进行比较,结果表明在迭代速度、聚类精度以及内存占比上,改进后的AFSA-RSK算法都要更优,更适合在结构面分组方面的应用.

Aiming at the shortcomings of the conventional classification method of structural plane production,a new structural plane classification algorithm was proposed.Based on the structural plane classification of K-Means algorithm,the AFSA-RSK structural surface classification algorithm is established by combining the artificial fish swarm algorithm(AFSA)with the K-Means algorithm.The powerful optimization ability of the fish swarm algorithm is used to replace the K-Means algorithm to search for the structural surface set,and clustering by K-Means algorithm.After the clustering is completed,the corresponding parameters are selected to evaluate the clustering effect.According to the existing problems,the step size and visual field of the fish swarm algorithm are modified,the accuracy of finding the cluster is improved,and the clustering process is dynamically adjusted.Comparing the improved AFSA-RSK algorithm with other algorithms,it can be obtained that the improved AFSA-RSK algorithm is better in iterative speed,clustering precision and memory ratio,and it is more suitable for application in structural plane grouping.