The dynamics of collective sorting robot-like ants and ant-like robots
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
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
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Improved Ant-Based Clustering and Sorting
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
AntNet: distributed stigmergetic control for communications networks
Journal of Artificial Intelligence Research
Computer Methods and Programs in Biomedicine
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A new fitness estimation strategy for particle swarm optimization
Information Sciences: an International Journal
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Ant colonies behavior and their self-organizing capabilities have been popularly studied, and various swarm intelligence models and clustering algorithms also have been proposed. Unfortunately, the cluster number is often too high and convergence is also slow. We put forward a novel structure-attractor, which actively attracts and guides the ant's behavior, and implement an efficient strategy to adaptively control the clustering behavior. Our experiments show that swarm intelligence clustering algorithm based on attractor (SICABA for short) greatly improves the convergence speed and clustering quality compared with LF and also has many notable virtues such as flexibility, decentralization compared with conventional algorithms.