### 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 1 |

### ASJC Scopus subject areas

- Software
- Hardware and Architecture
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering
- Artificial Intelligence

## Fingerprint Dive into the research topics of 'Neural computing for the m-way graph partitioning problem'. Together they form a unique fingerprint.

## Cite this

*IEICE Transactions on Information and Systems*,

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