English, Article edition: IMPROVED PROJECTION HOPFIELD NETWORK FOR THE QUADRATIC ASSIGNMENT PROBLEM KEIJI TATSUMI; TETSUZO TANINO

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Bookmark: http://trove.nla.gov.au/version/36681
Physical Description
  • article
Language
  • English

Edition details

Title
  • IMPROVED PROJECTION HOPFIELD NETWORK FOR THE QUADRATIC ASSIGNMENT PROBLEM
Author
  • KEIJI TATSUMI
  • TETSUZO TANINO
Physical Description
  • article
Notes
  • 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, Smith et 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.
  • Quadratic assignment problem, continuous-valued Hopfield neural networks, projection method, stability, convergence property
  • RePEc:wsi:ijitdm:v:07:y:2008:i:01:p:53-70
Language
  • English
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