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- W2088797869 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, 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." @default.
- W2088797869 created "2016-06-24" @default.
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- W2088797869 date "2008-03-01" @default.
- W2088797869 modified "2023-09-27" @default.
- W2088797869 title "IMPROVED PROJECTION HOPFIELD NETWORK FOR THE QUADRATIC ASSIGNMENT PROBLEM" @default.
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- W2088797869 doi "https://doi.org/10.1142/s0219622008002818" @default.
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