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A Quickly Searching Algorithm for Optimization Problems Based on Hysteretic Transiently Chaotic Neural Network

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Advances in Neural Networks – ISNN 2007 (ISNN 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4492))

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Abstract

This paper presents a fast algorithm based on the hysteretic transiently chaotic neural network (HTCNN) model for solving optimization problems. By using hysteretic activation function, HTCNN has higher ability of overcoming drawbacks that suffer from the local minimum. Meanwhile, in order to avoid oscillation and offer a considerable acceleration of converging to the optimal solution, a fast speed strategy is involved in HTCNN. Numerical simulation of a combinatorial optimization problem-assignment problem shows that HTCNN with fast speed strategy (FHTCNN) can overcome drawbacks that suffer from the local minimum and find the global optimal solutions quickly.

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Authors and Affiliations

  1. School of Management, Tianjin University, Tianjin 300072, China

    Xiuhong Wang

  2. Department of Biomedical Engineering, Tianjin Medical University, Tianjin 300070, China

    Qingli Qiao

Authors
  1. Xiuhong Wang
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  2. Qingli Qiao
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Editor information

Derong Liu Shumin Fei Zengguang Hou Huaguang Zhang Changyin Sun

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© 2007 Springer Berlin Heidelberg

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Wang, X., Qiao, Q. (2007). A Quickly Searching Algorithm for Optimization Problems Based on Hysteretic Transiently Chaotic Neural Network. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_10

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  • DOI: https://doi.org/10.1007/978-3-540-72393-6_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72392-9

  • Online ISBN: 978-3-540-72393-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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