Neural Computing for the m-Way Graph Partitioning Problem

Takayuki SAITO  Yoshiyasu TAKEFUJI  

IEICE TRANSACTIONS on Information and Systems   Vol.E80-D   No.9   pp.942-947
Publication Date: 1997/09/25
Online ISSN: 
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Issue on Architectures, Algorithms and Networks for Massively Parallel Computing)
Category: Algorithms
neural network,  graph partitioning,  heuristic algorithm,  combinatorial optimization,  

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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 undirected 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.