Solving ability of Hopfield neural network with chaotic noise and burst noise for quadratic assignment problem

2003 ◽  
Author(s):  
Y. Uwate ◽  
Y. Nishio ◽  
T. Ueta ◽  
T. Kawabe ◽  
T. Ikeguchi
Keyword(s):  
Neural Network ◽  
Assignment Problem ◽  
Quadratic Assignment Problem ◽  
Hopfield Neural Network ◽  
Quadratic Assignment ◽  
Burst Noise

IMPROVED PROJECTION HOPFIELD NETWORK FOR THE QUADRATIC ASSIGNMENT PROBLEM

2008 ◽  
Vol 07 (01) ◽  
pp. 53-70 ◽  
Author(s):  
KEIJI TATSUMI ◽  
TETSUZO TANINO
Keyword(s):  
Assignment Problem ◽  
Feasible Solution ◽  
Quadratic Assignment Problem ◽  
Hopfield Neural Network ◽  
Hopfield Network ◽  
Quadratic Assignment ◽  
Feasible Region ◽  
Projection Technique ◽  
Proposed Model ◽  
Theoretical Results

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.

The Quadratic Assignment Problem

1963 ◽  
Vol 9 (4) ◽  
pp. 586-599 ◽  
Author(s):  
Eugene L. Lawler
Keyword(s):  
Assignment Problem ◽  
Quadratic Assignment Problem ◽  
Quadratic Assignment
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