1996 年 116 巻 2 号 p. 296-301
When the travelling salesman problem (TSP) is solved by the Hopfield Neural Network with analog output, it is difficult to determine two parameters of its energy function, that is, a penalty factor and a temperature. In this paper, by numerical experiments, we investigate the influence of both parameters upon the quality of _??_ solutions, and propose a method where the penalty factor is controlled so as to obtain a feasible solution and the temperature is optimized by the golden section search.
Computational results for the TSPs with random 5_??_40 cities, the proposed method improves the quality of the solution by 48% on the average, compared with the conventional method.
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