Xiong Zhuang

Multi-elitist immune clonal quantum clustering algorithm

The quantum clustering (QC) algorithm suffers from the issues of getting stuck in local extremes and computational bottleneck when handling large-size image segmentation. By embedding a potential evolution formula into affinity function calculation of multi-elitist immune clonal optimization, and updating the cluster center based on the distance matrix, the multi-elitist immune clonal quantum clustering algorithm (ME-ICQC) is proposed in this paper. In the proposed framework, elitist population is composed of the individuals with high affinity, which is considered to play dominant roles in the evolutionary process. It can help to find the global optimal solution or near-optimal solution for most tested tasks. The diversity of population can be well maintained by general subgroup evolution of ME-ICQC. These different functions are implemented by the dissimilar mutation strategies or crossover operators. The bi-group exchanges the information of excellence antibodies using the hypercube co-evolution operation. Compared with existing algorithms, the ME-ICQC achieves an improved clustering accuracy with more stable convergence, but it is not significantly better than other optimization techniques combined with QC. Also, the experimental results also show that our algorithm performs well on multi-class, parameters-sensitive and large-size datasets.

Quantum Immune Fast Spectral Clustering for SAR Image Segmentation

Spectral clustering algorithm suffers from memory use and computational time bottleneck when handling large-scale image segmentation. By optimizing the selection of representative points before spectral embedding, a fast spectral clustering method with quantum immune optimization is proposed. The incorporation of quantum computing and immune clonal selection theory makes the selection of representative points more reasonable. The empirical study on the University of California Irvine standard data set clustering and synthetic aperture radar image segmentation demonstrates the efficiency of our algorithm and the capability to deal with large-scale data rapidly.

SAR image segmentation using quantum clonal selection clustering

A novel clustering algorithm is proposed, which is derived from physical intuition of quantum mechanics and biological principle based on immune clonal selection. As extension ideas of scale-space clustering and support vector clustering, quantum clustering method deduces the clustering allocation by gradient descent, which is prone to getting stuck in local extremes. By designing a novel and high-efficiency affinity function, we adopt an immune clonal selection algorithm with elite preservation strategy to search the global optimum. The experimental results on texture images and SAR images segmentation we demonstrate show that quantum clonal selection clustering method performs well both in precision and efficiency.