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Improved Projection Hopfield Network For The Quadratic Assignment Problem

Author

Listed:
  • KEIJI TATSUMI

    (Division of Electrical, Electronic and Information Engineering, Graduate School of Engineering, Osaka University, Yamada-Oka 2-1, Suita, Osaka 565-0871, Japan)

  • TETSUZO TANINO

    (Division of Electrical, Electronic and Information Engineering, Graduate School of Engineering, Osaka University, Yamada-Oka 2-1, Suita, Osaka 565-0871, Japan)

Abstract

The continuous-valued Hopfield neural network (CHN) is a popular and powerful metaheuristic method for combinatorial optimization. However, it is difficult to select appropriate penalty parameters for constraints so as to obtain a feasible and desirable solution by CHN. Thus, various improved models have been proposed. Matsuda proposed a CHN named optimal CHN and showed theoretical results on selecting parameters. On the other hand, Smithet al.proposed the projection CHN which projects a solution onto the feasible region and thus needs not select penalty parameters.In this paper, we point out some drawbacks of these two models and propose a new CHN with an efficient projection technique for the quadratic assignment problem, which overcomes these drawbacks. Moreover, we show that the proposed model can always find a feasible solution and that it has the local convergence property. Finally, we verify advantages of the proposed model through some numerical experiments.

Suggested Citation

  • Keiji Tatsumi & Tetsuzo Tanino, 2008. "Improved Projection Hopfield Network For The Quadratic Assignment Problem," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 7(01), pages 53-70.
    • Handle: RePEc:wsi:ijitdm:v:07:y:2008:i:01:n:s0219622008002818
    DOI: 10.1142/S0219622008002818
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