Natural-Inspired Data Clustering: A Hybridization between Ant Clustering and Particle Swarm Optimization.

摘要: The clustering algorithms have evolved over the last decade. With continuous success of natural inspired in solving many engineering problems, it is imperative to scrutinize these methods applied data clustering. These naturally are mainly stochastic search and optimization techniques, guided by principles collective behavior self-organization insect swarms. parameters setting ant colony determine each critical for fast convergence near optimal solutions task. This us explore techniques automatically learning a given We devised implemented hybrid Ant-Colony algorithm, which uses particle swarm algorithm early stages ‘breed’ population ants possessing behavioral parameter settings problem. converges rapidly nearly that maximize ant-colony behavior.