Solving Maximum Cut Problem Using Improved Hopfield Neural Network

Rong-Long WANG; Zheng TANG; Qi-Ping CAO

Solving Maximum Cut Problem Using Improved Hopfield Neural Network

Rong-Long WANG, Zheng TANG, Qi-Ping CAO

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The goal of the maximum cut problem is to partition the vertex set of an undirected graph into two parts in order to maximize the cardinality of the set of edges cut by the partition. The maximum cut problem has many important applications including the design of VLSI circuits and communication networks. Moreover, many optimization problems can be formulated in terms of finding the maximum cut in a network or a graph. In this paper, we propose an improved Hopfield neural network algorithm for efficiently solving the maximum cut problem. A large number of instances have been simulated. The simulation results show that the proposed algorithm is much better than previous works for solving the maximum cut problem in terms of the computation time and the solution quality.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E86-A No.3 pp.722-729

Publication Date: 2003/03/01

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Type of Manuscript: PAPER

Category: Neural Networks and Bioengineering

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Rong-Long WANG, Zheng TANG, Qi-Ping CAO, "Solving Maximum Cut Problem Using Improved Hopfield Neural Network" in IEICE TRANSACTIONS on Fundamentals, vol. E86-A, no. 3, pp. 722-729, March 2003, doi: .
Abstract: The goal of the maximum cut problem is to partition the vertex set of an undirected graph into two parts in order to maximize the cardinality of the set of edges cut by the partition. The maximum cut problem has many important applications including the design of VLSI circuits and communication networks. Moreover, many optimization problems can be formulated in terms of finding the maximum cut in a network or a graph. In this paper, we propose an improved Hopfield neural network algorithm for efficiently solving the maximum cut problem. A large number of instances have been simulated. The simulation results show that the proposed algorithm is much better than previous works for solving the maximum cut problem in terms of the computation time and the solution quality.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1587/e86-a_3_722/_p

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@ARTICLE{e86-a_3_722,
author={Rong-Long WANG, Zheng TANG, Qi-Ping CAO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Solving Maximum Cut Problem Using Improved Hopfield Neural Network},
year={2003},
volume={E86-A},
number={3},
pages={722-729},
abstract={The goal of the maximum cut problem is to partition the vertex set of an undirected graph into two parts in order to maximize the cardinality of the set of edges cut by the partition. The maximum cut problem has many important applications including the design of VLSI circuits and communication networks. Moreover, many optimization problems can be formulated in terms of finding the maximum cut in a network or a graph. In this paper, we propose an improved Hopfield neural network algorithm for efficiently solving the maximum cut problem. A large number of instances have been simulated. The simulation results show that the proposed algorithm is much better than previous works for solving the maximum cut problem in terms of the computation time and the solution quality.},
keywords={},
doi={},
ISSN={},
month={March},}

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TY - JOUR
TI - Solving Maximum Cut Problem Using Improved Hopfield Neural Network
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 722
EP - 729
AU - Rong-Long WANG
AU - Zheng TANG
AU - Qi-Ping CAO
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E86-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2003
AB - The goal of the maximum cut problem is to partition the vertex set of an undirected graph into two parts in order to maximize the cardinality of the set of edges cut by the partition. The maximum cut problem has many important applications including the design of VLSI circuits and communication networks. Moreover, many optimization problems can be formulated in terms of finding the maximum cut in a network or a graph. In this paper, we propose an improved Hopfield neural network algorithm for efficiently solving the maximum cut problem. A large number of instances have been simulated. The simulation results show that the proposed algorithm is much better than previous works for solving the maximum cut problem in terms of the computation time and the solution quality.
ER -