Evolving Annular Sorting in Ant-Like Agents

Vik, André Heie

doi:10.1007/11553090_60

Cited by 3 publications

(5 citation statements)

References 10 publications

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“…Collective robotics suggests itself as the obvious technical application of such methods (see Hartmann 2005;Vik 2005 for some recent work), but the approach can also be used for data clustering (Lumer and Faieta 1994). It seems particularly suitable for applications such as network clustering where centralized clustering is not feasible (Ekola et al 2004;Merkle et al 2004).…”

Section: Application To Data Clusteringmentioning

confidence: 98%

Ant-based and swarm-based clustering

2007

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Clustering with swarm-based algorithms is emerging as an alternative to more conventional clustering methods, such as hierarchical clustering and k-means. Ant-based clustering stands out as the most widely used group of swarm-based clustering algorithms. Broadly speaking, there are two main types of ant-based clustering: the first group of methods directly mimics the clustering behavior observed in real ant colonies. The second group is less directly inspired by nature: the clustering task is reformulated as an optimization task and general purpose ant-based optimization heuristics are utilized to find good or nearoptimal clusterings. This papers reviews both approaches and places these methods in the wider context of general swarm-based clustering approaches.

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Section: Application To Data Clusteringmentioning

confidence: 98%

Ant-based and swarm-based clustering

2007

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“…Initial tests with this system produced poor results but results improved when a genetic algorithm was used to select parameter values. Two subsequent models (Hartmann, 2005;Vik, 2005) both use a neural network controller for individual ants, with network weights being evolved using a genetic algorithm. These models have been successfully applied to the problems of clustering and annular sorting of objects (with spatial restrictions imposed, see the later discussion.)…”

Section: Annular Sortingmentioning

confidence: 99%

“…This behaviour, when observed in Temnothorax unifasciatus [1] (Franks and Sendova-Franks, 1992), leads to the formation of a single cluster of offspring made up of concentric rings of brood items, with the youngest items (eggs and micro-larvae) being tightly packed at the centre, and successively larger larvae arranged in increasingly wider-spaced bands moving out from the centre of the cluster. Models of this behaviour have yielded new algorithmic solutions to the problem of annular sorting (Scholes et al, 2004;Wilson et al, 2004;Hartmann, 2005;Vik, 2005;Scheidler et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Swarm‐based spatial sorting

Amos

Don

2008

International Journal of Intelligent Computing and Cybernetics

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International Journal of Intelligent Computing and Cybernetics (accepted)Purpose -To present an algorithm for spatially sorting objects into an annular structure. Design/Methodology/Approach -A swarm-based model that requires only stochastic agent behaviour coupled with a pheromone-inspired "attraction-repulsion" mechanism. Findings -The algorithm consistently generates high-quality annular structures, and is particularly powerful in situations where the initial configuration of objects is similar to those observed in nature. Research limitations/implications -Experimental evidence supports previous theoretical arguments about the nature and mechanism of spatial sorting by insects. Practical implications -The algorithm may find applications in distributed robotics. Originality/value -The model offers a powerful minimal algorithmic framework, and also sheds further light on the nature of attraction-repulsion algorithms and underlying natural processes.

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“…Initial tests with this system produced poor results but results improved when a genetic algorithm was used to select parameter values. Two subsequent models, due to Hartmann [8] and Vik [10], both use a neural network controller for individual ants, with network weights being evolved using a genetic algorithm. These models have been successfully applied to the problems of clustering and annular sorting of objects (with spatial restrictions imposed, see the later discussion.)…”

Section: Annular Sortingmentioning

confidence: 99%

“…This behaviour, when observed in Temnothorax unifasciatus 1 [6], leads to the formation of a single cluster of offspring made up of concentric rings of brood items, with the youngest items (eggs and micro-larvae) being tightly packed at the centre, and successively larger larvae arranged in increasingly wider-spaced bands moving out from the centre of the cluster. Models of this behaviour have yielded new algorithmic solutions to the problem of annular sorting [7], [8], [9], [10], [11].…”

Section: Introductionmentioning

confidence: 99%

An ant-based algorithm for annular sorting

Amos

Don

2007

2007 IEEE Congress on Evolutionary Computation

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We present an ant-based algorithm for spatially sorting objects into an annular structure. The model is minimal, as it requires only stochastic ant behaviour coupled with a pheromone-inspired "attraction-repulsion" mechanism. This is the first annular sorting algorithm to consider the problem of objects with non-uniform size, as well as the situation where objects are pre-sorted. The algorithm consistently generates high-quality annular structures, and is particularly powerful in situations where the initial configuration of objects is similar to those observed in nature. We suggest that this model provides a powerful algorithmic framework, as well as a contribution to the study of "attraction-repulsion" models of animal behaviour. 142 1-4244-1340-0/07/$25.00 c 2007 IEEE

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Evolving Annular Sorting in Ant-Like Agents

Cited by 3 publications

References 10 publications

Ant-based and swarm-based clustering

Ant-based and swarm-based clustering

Swarm‐based spatial sorting

An ant-based algorithm for annular sorting

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