DE-MC:A Membrane Clustering Algorithm Based on Differential Evolution Mechanism
A clustering algorithm under the framework of membrane computing is proposed in this paper, which integrates the differential evolution mechanism to its evolution rules. The used P system is a celllike P system of two-layer nested structure: a skin membrane contains several elementary membranes. Each object in elementary membranes represents a group of cluster centers. Objects in the system are evolved by differential evolution mechanism, and then the global optimal object in the skin membrane is updated by the best objects in all elementary membranes. The cell-like P system can automatically find the best cluster centers for a data set. The proposed DE-MC algorithm is evaluated on an artificial data set and a real-life data set and is further compared with classical k-means algorithm, GA-based clustering algorithm and DE-based clustering algorithm respectively. The comparison results reveal that the proposed DE-MC algorithm is superior to other three clustering algorithms in terms of clustering quality and robustness.
Membrane computing P systems Clustering algorithm Differential evolution
Jiarong Zhang Hong Peng Yang Jiang Xiaoli Huang Jun Wang
Center for Radio Administration and Technology Development,Xihua University,Chengdu,610039,China School of Electrical and Information Engineering,Xihua University,Chengdu,Sichuan,610039,China
国际会议
成都
英文
385-398
2013-11-04（万方平台首次上网日期，不代表论文的发表时间）