Comparison of the Traveling Salesman Problem Analysis Using Neural Network Method
Aeri Rachmad, Eka Mala Sari Rochman, Dwi Kuswanto, Iwan Santosa, Rinci Kembang Hapsari, Tutuk Dwi Indriyani, Endah Purwanti · Proceedings of the International Conference on Science and Technology (ICST 2018) · 2018
Traveling sales problem (TSP) is one of the classical optimization problems and NP-complete.The Challenge in implementing TSP is how to determine the shortest distance from a traveling route of N cities where each N city visited precisely once at time.In this paper, we attempt to analyze the completion of TSP using an artificial neural network approach.In network, weights are determined to represent problem boundaries and optimize the completion function.The approach of artificial neural networks used is a network with Hopfield algorithm and Simulated Annealing algorithm.The solution with the Hopfield algorithm has the complexity of the 4n 4 + 16n 3 algorithm, while the complexity of the Simulated Annealing algorithm is 10n2.Judging from the use of memory in the implementation, the application of annealing algorithm is better than the Hopfield algorithm.but both are algorithms that "no works in place".