Million city traveling salesman problem solution by divide and conquer clustering with adaptive resonance neural networks

Mulder, Samuel A.; Wunsch, Donald C.

doi:10.1016/s0893-6080(03)00130-8

Cited by 79 publications

(31 citation statements)

References 3 publications

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“…Mulder and Wunsch applied a divide-and-conquer clustering technique, with ART networks, to scale the problem to a million cities [208]. The divide and conquer paradigm gives the flexibility to hierarchically break large problems into arbitrarily small clusters depending on what tradeoff between accuracy and speed is desired.…”

Section: Traveling Salesman Problemmentioning

confidence: 99%

Survey of Clustering Algorithms

Wunsch

2005

IEEE Trans. Neural Netw.

4,980

2,338

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Abstract-Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.Index Terms-Adaptive resonance theory (ART), clustering, clustering algorithm, cluster validation, neural networks, proximity, self-organizing feature map (SOFM).

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Section: Traveling Salesman Problemmentioning

confidence: 99%

Survey of Clustering Algorithms

Wunsch

2005

IEEE Trans. Neural Netw.

4,980

2,338

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“…To cope with the size issue, they had to use parallelized computers to solve the problem. Mulder and Wunsch [29] divided huge TSP by clustering algorithms and solved each cluster with adaptive resonance neural network. They were claiming that proposed divide and conquer paradigm can increase scalability and parallelism.…”

Section: Related Workmentioning

confidence: 99%

Parallelized neural network system for solving Euclidean traveling salesman problem

Avşar

Aliabadi

2015

Applied Soft Computing

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“…The more common form of the problem is the optimization problem of trying to find the shortest Hamiltonian cycle, and in particular, the most common is the Euclidean version, where the vertices and edges all lie in the plane. Mulder and Wunsch applied a divideand-conquer clustering technique, with ART networks, to scale the problem to a million cities [59], and later, to 25 million cities [85]. The divide and conquer paradigm gives the flexibility to hierarchically break large problems into arbitrarily small clusters depending on what trade-off between accuracy and speed is desired.…”

Section: Traveling Salesman Problemmentioning

confidence: 99%

Computational Intelligence in Clustering Algorithms, With Applications

Wunsch

2007

Algorithms for Approximation

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-Cluster analysis plays an important role for understanding various phenomena and exploring the nature of obtained data. A remarkable diversity of ideas, in a wide range of disciplines, has been applied to clustering research. Here, we survey clustering algorithms in computational intelligence, particularly based on neural networks and kernel-based learning. We further illustrate their applications in five real world problems.

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Million city traveling salesman problem solution by divide and conquer clustering with adaptive resonance neural networks

Cited by 79 publications

References 3 publications

Survey of Clustering Algorithms

Survey of Clustering Algorithms

Parallelized neural network system for solving Euclidean traveling salesman problem

Computational Intelligence in Clustering Algorithms, With Applications

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