Data Clustering Using Hybridization Strategies of Continuous Ant Colony Optimization, Particle Swarm Optimization and Genetic Algorithm

Abstract

Nowadays, clustering plays a critical role in most research areas such as engineering, medicine, biology, data mining, etc. Evolutionary algorithms, including continuous ant colony optimization, particle swarm optimization, and genetic algorithms, have been employed for data clustering. To improve searching skills, this paper examines four strategies, combining of continuous ant colony optimization and particle swarm optimization, and proposes a strategy which is a combination of these two algorithms with genetic algorithm. Available methods and the proposed method were implemented over several sets of benchmark data to assess the validity. Results were compared with the results of continuous ant colony optimization and particle swarm optimization. The high capacity and resistance of combined methods are obvious according to results.