IMPROVED PROJECTION HOPFIELD NETWORK FOR THE QUADRATIC ASSIGNMENT PROBLEM

Keiji Tatsumi () and Tetsuzo Tanino ()
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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

International Journal of Information Technology & Decision Making (IJITDM), 2008, vol. 07, issue 01, 53-70

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.

Keywords: Quadratic assignment problem; continuous-valued Hopfield neural networks; projection method; stability; convergence property (search for similar items in EconPapers)
Date: 2008
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DOI: 10.1142/S0219622008002818

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