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On the hybrid neural network model for solving optimization problems

  • Fouad B. Chedid
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1120)

Abstract

A recent model of neural networks, named the Hybrid Neural Network Model (HN), for solving optimization problems appeared in [3]. In [3], the main algorithm called the Hybrid Network Updating Algorithm (HNUA) is used to drive the HN model. The best thing about the HNUA is that it reaches a feasible solution very quickly. Our argument here is that while the HNUA is very quick to satisfy the constraints, it guarantees very little in terms of the quality of the generated solution. In this paper we rewrite one of the steps in the HNUA so that the goal function is better served. we demonstrate our work using the traveling salesman problem as an example.

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References

  1. [1]
    M.R. Garey and D.S. Johnson, Computers and Intractability: A Guide to the Theory of NP-completeness. San Francisco, CA: Freeman, 1979.Google Scholar
  2. [2]
    J.J. Hopfield and D.W. Tank, “Neural Computation of Decisions in Optimization Problems,” Biological Cybernetics, vol. 52, pp. 1–25, 1985.PubMedGoogle Scholar
  3. [3]
    K.T. Sun and H.C. Fu, “A Hybrid Neural Network Model for Solving Optimization Problems,” IEEE Trans. on Computers, vol. 42, no. 2, pp. 218–227, Feb. 1993.CrossRefGoogle Scholar
  4. [4]
    Introduction to Neural Networks, Computer Simulations of Biological Intelligence,” California Scientific Software, Pasadena, CA, 1988.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Fouad B. Chedid
    • 1
  1. 1.Faculty of Computer ScienceTemple University JapanTokyoJapan

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