ペナルティ制御法によるTSPニューラルネットの実行可能性の改善

抄録

In this paper we theoretically analyze local minima of a neural network for solving the Travelling Salesman Problem (TSP) and find the neuronal matrix has at most one fired neuron on each row and column. We also find theoretical values of the penalty factor that can make local minima always feasible or always infeasible. Further, we propose a method to improve feasibility of the local minimum: the penalty factor is initially set to a very small value to obtain an infeasible tour with short subtours and, afterwards, is gradually increased finally to obtain a feasible trip.
Computational results for the TSPs with random 5_??_30 cities indicate that theoretical results are verified arid the proposed method improve the quality of the solution by 48% on the average compared with the conventional method.