Neural computing for the m-way graph partitioning problem

Takayuki Saito, Yoshiyasu Takefuji

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The graph partitioning problem is a famous combinatorial problem and has many applications including VLSI circuit design, task allocation in distributed computer systems and so on. In this paper, a novel neural network for the m-way graph partitioning problem is proposed where the maximum neuron model is used. The unidirected graph with weighted nodes and weighted edges is partitioned into several subsets. The objective of partitioning is to minimize the sum of weights on cut edges with keeping the size of each subset balanced. The proposed algorithm was compared with the genetic algorithm. The experimental result shows that the proposed neural network is better or comparable with the other existing methods for solving the m-way graph partitioning problem in terms of the computation time and the solution quality.

Original languageEnglish
Pages (from-to)942-947
Number of pages6
JournalIEICE Transactions on Information and Systems
VolumeE80-D
Issue number9
Publication statusPublished - 1997 Sep

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Neural networks
VLSI circuits
Distributed computer systems
Set theory
Neurons
Genetic algorithms

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Information Systems
  • Software

Cite this

Neural computing for the m-way graph partitioning problem. / Saito, Takayuki; Takefuji, Yoshiyasu.

In: IEICE Transactions on Information and Systems, Vol. E80-D, No. 9, 09.1997, p. 942-947.

Research output: Contribution to journalArticle

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