Fast Ant Colony Optimization for Clustering - Abba Suganda Girsang; Tjeng Wawan Cenggoro; Ko-Wei Huang (Institute of Advanced Engineering and Science, 2018)
Data clustering is popular data analysis approaches, which used to organizing data into sensible clusters based on similarity measure, where data within a cluster are similar to each other but dissimilar to that of another cluster. In the recently, the cluster problem has been proven as NP-hard problem, thus, it can be solved with meta-heuristic algorithms, such as the particle swarm optimization (PSO), genetic algorithm (GA), and ant colony optimization (ACO), respectively. This paper proposes an algorithm called Fast Ant Colony Optimization for Clustering (FACOC) to reduce the computation time of Ant Colony Optimization (ACO) in clustering problem. FACOC is developed by the motivation that a redundant computation is occurred in ACO for clustering. This redundant computation can be cut in order to reduce the computation time of ACO for clustering. The proposed FACOC algorithm was verified on 5 well-known benchmarks. Experimental result shows that by cutting this redundant computation, the computation time can be reduced about 28% while only suffering a small quality degradation.