Constrained Ant Colony Optimization for Data Clustering

Processes that simulate natural phenomena have successfully been applied to a number of problems for which no simple mathematical solution is known or is practicable. Such meta-heuristic algorithms include genetic algorithms, particle swarm optimization and ant colony systems and have received increasing attention in recent years. This paper extends ant colony systems and discusses a novel data clustering process using Constrained Ant Colony Optimization (CACO). The CACO algorithm extends the Ant Colony Optimization algorithm by accommodating a quadratic distance metric, the Sum of K Nearest Neighbor Distances (SKNND) metric, constrained addition of pheromone and a shrinking range strategy to improve data clustering. We show that the CACO algorithm can resolve the problems of clusters with arbitrary shapes, clusters with outliers and bridges between clusters.

• Type: Conference
• Year: 2004
• Where: PRICAI
• Authors: Shu-Chuan Chu, John F. Roddick, Che-Jen Su, Jeng-Shyang Pan
• Keywords: Ant Colony, Ant Colony Optimization, Ant Colony Systems, Artificial Intelligence, PRICAI 2004