Diversity and adaptation in populations of clustering ants
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
A new representation and operators for genetic algorithms applied to grouping problems
Evolutionary Computation
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Clustering with bio-inspired algorithms is emerging as an alternative to more conventional clustering techniques. In this paper, we propose a new bio-inspired divisive clustering algorithm FDClust (Artificial Fish based Divisive Clustering algorithm). FDClust takes inspiration from the social organization and the encounters of fish shoals. In this algorithm, each artificial fish (agents) is identified with one object to be clustered. Agents move randomly on the clustering environment and interact with neighboring agents in order to adjust their movement directions. Two Groups of similar objects will appear through the movement of agents in the same direction. The algorithm is tested and evaluated on several real benchmark databases. The obtained results are very interesting in comparison with Kmeans, Slink, Alink, Clink and Diana algorithms.