### 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 language | English |
---|---|

Pages (from-to) | 942-947 |

Number of pages | 6 |

Journal | IEICE Transactions on Information and Systems |

Volume | E80-D |

Issue number | 9 |

Publication status | Published - 1997 Sep |

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### ASJC Scopus subject areas

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

### Cite this

*IEICE Transactions on Information and Systems*,

*E80-D*(9), 942-947.

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

Research output: Contribution to journal › Article

*IEICE Transactions on Information and Systems*, vol. E80-D, no. 9, pp. 942-947.

}

TY - JOUR

T1 - Neural computing for the m-way graph partitioning problem

AU - Saito, Takayuki

AU - Takefuji, Yoshiyasu

PY - 1997/9

Y1 - 1997/9

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0031224360&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0031224360&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:0031224360

VL - E80-D

SP - 942

EP - 947

JO - IEICE Transactions on Information and Systems

JF - IEICE Transactions on Information and Systems

SN - 0916-8532

IS - 9

ER -