Investigating the Particle Swarm Optimization Clustering Method on Nucleic Acid Sequences

Particle swarm optimization (PSO) has been employed on several optimization problems, including the clustering problem. PSO has also been employed in the clustering of data of different structure and dimensionality. In this paper it is employed in the clustering of nucleic acid sequences. The application of clustering, as a statistical tool, in the analysis of data of varied complexity has been treated by several researchers. Besides PSO, distance-based algorithms have been widely proposed for the clustering problem. This paper investigates the efficiency of PSO clustering on nucleic acid sequences through the introduction of distance measures among which are the Euclidean distance measure, Manhattan distance, edit distance and the codon-based scoring method (COBASM). Subobjective weights were introduced to observe the behaviour of PSO under various conditions. From the result obtained, PSO-based clustering produces compact and well-separated clusters. However, the result varied with distance measure.