Spatial Clustering with Obstacles Constraints by Ant Colony Optimization and Quantum Particle Swarm Optimization
Xueping Zhang, Jianjun Wu, Haifang Si, Tengfei Yang, Yawei Liu · 2009
The paper proposed a novel ant colony optimization (ACO) and quantum particle swarm optimization (QPSO) method for spatial clustering with obstacles constraints (SCOC). We first developed AQPGSOD using ACO and QPSO based on grid model to obtain obstructed distance, and then we presented a new QPKSCOC based on QPSO and K-Medoids to cluster spatial data with obstacles. The experimental results show that AQPGSOD is effective, and QPKSCOC can not only give attention to higher local constringency speed and stronger global optimum search, but also get down to the obstacles constraints and practicalities of spatial clustering.