New results on an ant-based heuristic for highlighting the organization of large graphs

Kuntz, Pascale; Snyers, Dominique

doi:10.1109/cec.1999.782654

Cited by 25 publications

(17 citation statements)

References 16 publications

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“…The Ant System (AS) was first introduced by Dorigo and his colleagues [18,19,20,23]. Since then, ACO algorithms have been applied to different problems such as the traveling salesman problem (TSP) [4,6,9,10,11,21,22,23,25,30,46], the quadratic assignment problem (QAP) [40,52], the generalized assignment problem [48], the vehicle routing problem [5,7,16,17,27], telecommunication networks [15], graph coloring [13,34,35], scheduling [1,12,14,32,42,43], the shortest supersequence problem [44,45], the Hamiltonian graph problem [55], the multiple knapsack problem [37], the sequential ordering problem [26], the redundancy allocation problem [39], water distribution network design [41], the constraint satisfaction problem [51], and continuous function problems [2,3,57]. Local search plays an important role in improving the solution quality of ACO algorithms.…”

Section: Background Of the Ant Colony Methodsmentioning

confidence: 99%

An Ant Colony Approach to the Orienteering Problem

Liang

Smith

2006

Journal of the Chinese Institute of Industrial Engineers

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This paper develops an ant colony optimization approach to the orienteering problem, a general version of the well-known traveling salesman problem with many relevant applications in industry. Based on mainstream ant colony ideas, an unusual sequenced local search and a distance based penalty function are added to result in a method that is convincingly shown to be the best heuristic published for this problem class. Results on 67 test problems from the literature show that the ant colony method performs as well or better in all cases and does so at very modest computational cost. Furthermore, the ant colony method is insensitive to seed, problem instance, problem size and degree of constraint.

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Section: Background Of the Ant Colony Methodsmentioning

confidence: 99%

An Ant Colony Approach to the Orienteering Problem

Liang

Smith

2006

Journal of the Chinese Institute of Industrial Engineers

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“…This introduced standard Ant Clustering Algorithm (ACA). It has subsequently been used for numerical data analysis (Lumer and Faieta, 1994), data-mining (Lumer and Faieta, 1995), graph-partitioning (Kuntz and Snyers, 1994, Kuntz and Snyers, 1999, Kuntz et al, 1998 and text-mining (Handl and Meyer B, 2002,Hoe et al, 2002,Ramos and Merelo, 2002. Many authors Meyer B, 2002,Ramos et al, 2002) proposed a number of modifications to improve the convergence rate and to get optimal number of clusters.…”

Section: The Ant Colony Based Clustering Algorithmsmentioning

confidence: 99%

Pattern Clustering Using a Swarm Intelligence Approach

Das

Abraham

2009

Data Mining and Knowledge Discovery Handbook

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Summary. Clustering aims at representing large datasets by a fewer number of prototypes or clusters. It brings simplicity in modeling data and thus plays a central role in the process of knowledge discovery and data mining. Data mining tasks, in these days, require fast and accurate partitioning of huge datasets, which may come with a variety of attributes or features. This, in turn, imposes severe computational requirements on the relevant clustering techniques. A family of bio-inspired algorithms, well-known as Swarm Intelligence (SI) has recently emerged that meets these requirements and has successfully been applied to a number of real world clustering problems. This chapter explores the role of SI in clustering different kinds of datasets. It finally describes a new SI technique for partitioning a linearly non-separable dataset into an optimal number of clusters in the kernel-induced feature space. Computer simulations undertaken in this research have also been provided to demonstrate the effectiveness of the proposed algorithm.

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“…In several articles the algorithm's use for the two-dimensional visualization of document collections in the form of topic maps [14,27,13] and its applicability to graph partitioning [19,21,20] were investigated. In one of those articles, [20], the Pearson correlation of the dissimilarities between pairs of data in data space and their respective spatial distances on the grid was measured in order to support the claimed capability of the ants to generate an isometric embedding. While the results obtained were far from optimal, the deficit from a perfect correlation of 1.0 was due -it was argued -to the gathering of ''vertices with a small dissimilarity'' without any further discrimination (that is, merely a result of defects in highly local topolog y preservation).…”

Section: Ant-based Clustering and Sortingmentioning

confidence: 99%

“…Ant-based clustering and sorting -the focus of our work-is a local, distributed heuristic that has been applied to both of the above tasks: clustering [26] and topographic mapping [23,18,21,20,13,14,27]. Indeed, some works claim that, like self-organizing maps, ant-based clustering and sorting is capable of performing both of these tasks simultaneously [22,20].…”

Section: Introductionmentioning

confidence: 99%

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Ant-Based Clustering and Topographic Mapping

2006

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Ant-based clustering and sorting is a nature-inspired heuristic first introduced as a model for explaining two types of emergent behavior observed in real ant colonies. More recently, it has been applied in a data-mining context to perform both clustering and topographic mapping. Early work demonstrated some promising characteristics of the heuristic but did not extend to a rigorous investigation of its capabilities. We describe an improved version, called ATTA, incorporating adaptive, heterogeneous ants, a time-dependent transporting activity, and a method (for clustering applications) that transforms the spatial embedding produced by the algorithm into an explicit partitioning. ATTA is then subjected to the most rigorous experimental evaluation of an ant-based clustering and sorting algorithm undertaken to date: we compare its performance with standard techniques for clustering and topographic mapping using a set of analytical evaluation functions and a range of synthetic and real data collections. Our results demonstrate the ability of ant-based clustering and sorting to automatically identify the number of clusters inherent in a data collection, and to produce high quality solutions; indeed, we show that it is particularly robust for clusters of differing sizes and for overlapping clusters. The results obtained for topographic mapping are, however, disappointing. We provide evidence that the solutions generated by the ant algorithm are barely topolog y-preserving, and we explain in detail why results have -in spite of this -been misinterpreted (much more positively) in previous research.

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New results on an ant-based heuristic for highlighting the organization of large graphs

Cited by 25 publications

References 16 publications

An Ant Colony Approach to the Orienteering Problem

An Ant Colony Approach to the Orienteering Problem

Pattern Clustering Using a Swarm Intelligence Approach

Ant-Based Clustering and Topographic Mapping

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