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A New Parallel Improvement Algorithm for Maximum Cut Problem

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Book cover Advances in Neural Networks – ISNN 2004 (ISNN 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3173))

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Abstract

The goal of 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. Enlightened by the elastic net method that was introduced by Durbin and Willshaw for finding shortest route for the Traveling Salesman Problem (TSP), we propose a new parallel algorithm for the maximum cut problem. A large number of instances are simulated to verify the proposed algorithm. The effectiveness of the proposed algorithm is confirmed by the simulation results.

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References

  1. Hsu, C.-P.: Minimum-Via Topological Routing. IEEE Trans. Compu.-Aided Des. Integr. Circuits Syst. 2(4), 235–246 (1983)

    Article  Google Scholar 

  2. Lee, K.C., Funabiki, N., Takefuji, Y.: A Parallel Improvement Algorithm For The Bipartite Subgraph Problem. IEEE Trans. Neural Networks 3(1), 139–145 (1992)

    Article  Google Scholar 

  3. Hopfield, J.J., Tank, D.W.: “Neural” Computation Of Decisions In Optimization Problems. Bio. Cybern. 52, 142–152 (1985)

    MathSciNet  Google Scholar 

  4. Durbin, R., Willshaw, D.: An Analogue Approach Of The Traveling Salesman Problem Using An Elastic Net Method. Nature 326, 689–691 (1987)

    Article  Google Scholar 

  5. Durbin, R., Szeliski, R., Yuille, A.: An Analysis Of The Elastic Net Approach To The Traveling Salesman Problem. Neural Computation 1, 348–358 (1989)

    Article  Google Scholar 

  6. Takefuji, Y., Lee, K.C.: Artificial Neural Networks For Four-Coloring Map Problems And K-Colorability Problems. IEEE Trans. Circuits Syst. 38(3), 326–333 (1991)

    Article  Google Scholar 

  7. McCulloch, W.S., Pitts, W.H.: A Logical Calculus Of Ideas Immanent In Nervous Activity. Bull. Math. Biophys 5, 115–133 (1943)

    Article  MATH  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Engineering, Toyama University, Toyama-shi, Japan, 930-8555

    Guangpu Xia, Zheng Tang, Jiahai Wang & Yong Li

  2. Faculty of Engineering, Fukui University, Japan, 910-8507

    Ronglong Wang

  3. Daxing Branch School of Beijing Radio & Television University, Beijing, China, 102600

    Guang’an Xia

Authors
  1. Guangpu Xia
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  2. Zheng Tang
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  3. Jiahai Wang
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  4. Ronglong Wang
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  5. Yong Li
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  6. Guang’an Xia
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Editor information

Editors and Affiliations

  1. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, China

    Fu-Liang Yin

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  3. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, Liaoning, China

    Chengan Guo

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© 2004 Springer-Verlag Berlin Heidelberg

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Xia, G., Tang, Z., Wang, J., Wang, R., Li, Y., Xia, G. (2004). A New Parallel Improvement Algorithm for Maximum Cut Problem. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_70

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  • DOI: https://doi.org/10.1007/978-3-540-28647-9_70

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22841-7

  • Online ISBN: 978-3-540-28647-9

  • eBook Packages: Springer Book Archive

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