A Method for the Travelling Salesman Problem by Controlling two Parameters of the Hopfield Neural Network
Nenso Setsu, Kenji Murakami, Takahumi Oohori, Kazuhisa Watanabe · IEEJ Transactions on Electronics Information and Systems · 1996
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.